A control method and system for an intra-aortic balloon counterpulsation system

Abstract

This invention provides a control method and system for an intra-aortic balloon counterpulsation (IABP) system, relating to the field of medical system control technology. The method provided by this invention, through a pre-trained neural network model, can rapidly and accurately determine the target cardiac cycle of the target user based on historical ECG monitoring data when the user experiences arrhythmia or poor ECG signal quality. Then, it controls the inflation and deflation of the IABP system's balloon according to the target cardiac cycle. Compared to related technologies where IABP systems are controlled by ECG triggering, the method provided by this invention effectively improves the safety and effectiveness of the IABP system and reduces user risks.

Description

Technical Field

[0001] This invention relates to the field of medical system control technology, and in particular to a control method and system for an intra-aortic balloon counterpulsation system. Background Technology

[0002] The intra-aortic balloon pump (IABP) system is a key component in interventional treatment of cardiovascular diseases. The IABP system assists the heart by controlling the inflation and deflation of a balloon, thereby controlling blood flow through the user's aorta through counterpulsation.

[0003] Currently, IABP systems assist the heart by using electrocardiogram (ECG) triggering. ECG triggering can be understood as synchronizing the inflation and deflation of the IABP bladder based on the user's cardiac electrophysiological activity. During each cardiac cycle, a series of ECG signals are generated. By monitoring these signals, the IABP system can accurately identify the systolic and diastolic phases of the heart and control the inflation and deflation of the IABP bladder accordingly. However, this ECG triggering method has certain drawbacks. In cases of arrhythmia or poor ECG signal quality, the triggering method may fail to accurately identify the user's cardiac cycle, leading to a desynchronization between the bladder inflation / deflation process and the user's cardiac activity. Furthermore, ECG signals can be affected by electromagnetic interference, causing signal distortion or false triggering, which can lead to IABP system control failure. Therefore, the IABP system suffers from issues of poor safety and effectiveness.

[0004] Therefore, there is an urgent need for an inflation control method and system for the aortic balloon counterpulsation (IABP) system, which can quickly and accurately determine the user's cardiac cycle when the user experiences arrhythmia or poor ECG signal quality, and then control the inflation and deflation of the IABP system's balloon based on the cardiac cycle, thereby improving the safety and effectiveness of the IABP system. Summary of the Invention

[0005] This invention provides a method and system for controlling the inflation of an aortic balloon counterpulsation (IABP) balloon. This system can quickly and accurately determine the user's cardiac cycle when the user experiences arrhythmia or poor ECG signal quality, and then control the inflation and deflation of the balloon of the IABP system based on the cardiac cycle, thereby improving the safety and effectiveness of the IABP system.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0007] Firstly, a control method for an intra-aortic balloon counterpulsation (IACP) system is provided, applied to the IACP system. The IACP system includes a gas cylinder, a pressure regulator, a solenoid valve, a processor, a motor, a cylinder, an air bag, and an electrocardiogram (ECG) monitoring module. The gas cylinder, cylinder, and air bag are connected via a gas path. The solenoid valve controls the gas from the gas cylinder to enter the air bag and cylinder. The pressure regulator is located between the gas cylinder and the solenoid valve, and controls the gas pressure output from the gas cylinder within a preset gas pressure threshold range. The processor is electrically connected to the motor and the ECG monitoring module, and the motor is connected to the cylinder. The method includes: the ECG monitoring module acquiring the ECG signal of the target user and sending the target user's ECG signal to the processor. The processor receives the target user's electrocardiogram (ECG) signal. If the amplitude of the target user's ECG signal is less than a preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than a preset waveform quality coefficient, the processor acquires the target user's historical ECG monitoring data, which includes ECG signals corresponding to multiple cardiac cycles within a target time period. The processor inputs the historical ECG monitoring data into a pre-trained neural network model to predict the target user's target cardiac cycle. The processor generates control commands based on the target user's target cardiac cycle. The processor controls the motor to operate according to the control commands, so that the motor adjusts the internal pressure of the cylinder to complete the contraction and expansion of the airbag.

[0008] In one possible implementation of the first aspect, the neural network model includes a classifier, a first recurrent neural network, and a second recurrent neural network, with the classifier connected to both the first and second recurrent neural networks. The classifier is used to determine the category corresponding to the historical ECG monitoring data, which includes normal heart rate or abnormal heart rate. It is also used to send historical ECG monitoring data categorized as normal heart rate to the first recurrent neural network and to send historical ECG monitoring data categorized as abnormal heart rate to the second recurrent neural network. The first recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as normal heart rate. The second recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as abnormal heart rate.

[0009] In one possible implementation of the first aspect, before the processor inputs historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user, the method further includes: the processor acquiring training data, which includes ECG signals corresponding to multiple cardiac cycles of each user among multiple users, and the category corresponding to each user; the processor sampling the training data to obtain multiple training samples and the category corresponding to each training sample, each training sample including feature data and label data, the feature data being the ECG signals of the first n-1 cardiac cycles among the ECG signals of any user in a continuous n cardiac cycles, and the label data being the ECG signal of the nth cardiac cycle among the ECG signals of a continuous n cardiac cycles; the processor training a classifier based on the multiple training samples and the category corresponding to each training sample to obtain a trained classifier; the processor training a first recurrent neural network based on training samples of the category of normal heart rate to obtain a trained first recurrent neural network; the processor training a second recurrent neural network based on training samples of the category of abnormal heart rate to obtain a trained second recurrent neural network, wherein the loss function of the first recurrent neural network and the second recurrent neural network is the root mean square error function.

[0010] In one possible implementation of the first aspect, the intra-aortic balloon counterpulsation system further includes a blood pressure monitoring module and a display module. The blood pressure monitoring module is electrically connected to the processor, and the display module is electrically connected to the processor. After the processor controls the motor to operate according to control instructions to adjust the internal pressure of the cylinder and complete the contraction and expansion of the balloon, the method further includes: the blood pressure monitoring module acquiring the arterial blood pressure value of the target user and sending the arterial blood pressure value of the target user to the processor; the processor generating a first alarm message when the arterial blood pressure value of the target user is not within a preset blood pressure threshold range; and the display module displaying the first alarm message, which is used to indicate that the arterial blood pressure value of the target user is abnormal.

[0011] In one possible implementation of the first aspect, the intra-aortic balloon counterpulsation system further includes an electronic pressure relief valve and multiple pressure sensors, which are disposed in the air passage between the cylinder and the balloon, and are electrically connected to a processor. The method further includes: the pressure sensors acquiring the pressure value inside the balloon and sending it to the processor; the processor controlling the electronic pressure relief valve to open when the pressure value inside the balloon exceeds a preset pressure threshold; and the method further includes: the processor generating a second alarm message when the difference between multiple pressure values ​​acquired by the multiple sensors between the cylinder and the balloon exceeds a preset pressure difference; and a display module displaying the second alarm message, which is used to indicate an abnormality in the pressure sensors.

[0012] In one possible implementation of the first aspect, the intra-aortic balloon counterpulsation system further includes a mechanical pressure relief valve disposed in the air passage between the cylinder and the balloon, the mechanical pressure relief valve being used to open when the pressure inside the balloon exceeds a preset pressure threshold.

[0013] The beneficial effects of this invention are as follows: The method provided by this invention, through a pre-trained neural network model, can quickly and accurately determine the target cardiac cycle of the target user based on historical ECG monitoring data when the target user experiences arrhythmia or poor ECG signal quality. Then, it inflates and deflates the IABP system's airbag according to the target cardiac cycle. Compared to related technologies that use ECG-triggered control methods, the method provided by this invention effectively improves the safety and effectiveness of the IABP system and reduces user risks. Furthermore, the method provided by this invention, by setting a pressure regulator, can control the gas output from the gas cylinder within a preset gas pressure threshold range, thereby accurately controlling the amount of gas output from the gas cylinder, and thus making the control of the airbag's contraction and expansion more precise. Finally, the method provided by this invention, by setting a mechanical pressure relief valve, can provide accurate early warning of the internal pressure value of the airbag in the event of electronic pressure relief valve failure, avoiding unnecessary danger to the user.

[0014] Secondly, embodiments of the present invention provide an intra-aortic balloon counterpulsation system, comprising a gas cylinder, a pressure regulator, a solenoid valve, a processor, a motor, a cylinder, an air bag, and an electrocardiogram (ECG) monitoring module; the gas cylinder, cylinder, and air bag are connected via a gas path; the solenoid valve controls the gas from the gas cylinder to enter the air bag and cylinder; the pressure regulator is positioned between the gas cylinder and the solenoid valve, and controls the gas pressure output from the gas cylinder within a preset gas pressure threshold range; the processor is electrically connected to the motor and the ECG monitoring module, and the motor is connected to the cylinder; the ECG monitoring module is used to: acquire the ECG signal of the target user and send the target user's ECG signal to the processor; the processor is used to: acquire historical ECG monitoring data of the target user when the amplitude of the target user's ECG signal is less than a preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than a preset waveform quality coefficient, the historical ECG monitoring data including ECG signals corresponding to multiple cardiac cycles of the target user within a target time period; and input the historical ECG monitoring data. A pre-trained neural network model predicts the target cardiac cycle of the target user; control commands are generated based on the target cardiac cycle; the motor is controlled according to the control commands to adjust the internal pressure of the cylinder, thereby completing the contraction and expansion of the airbag; the neural network model includes a classifier, a first recurrent neural network, and a second recurrent neural network, with the classifier connected to both; the classifier determines the category of historical ECG monitoring data, including normal or abnormal heart rate; it sends historical ECG monitoring data classified as normal heart rate to the first recurrent neural network and historical ECG monitoring data classified as abnormal heart rate to the second recurrent neural network; the first recurrent neural network outputs the corresponding target cardiac cycle based on the input historical ECG monitoring data classified as normal heart rate; the second recurrent neural network outputs the corresponding target cardiac cycle based on the input historical ECG monitoring data classified as abnormal heart rate.

[0015] In one possible implementation of the second aspect, before the processor inputs historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user, the processor specifically performs the following steps: acquiring training data, which includes ECG signals corresponding to multiple cardiac cycles of each user among multiple users, and the category corresponding to each user; sampling the training data to obtain multiple training samples and the category corresponding to each training sample, each training sample including feature data and label data, the feature data being the ECG signals of the first n-1 cardiac cycles out of n consecutive cardiac cycles of any user, and the label data being the ECG signal of the nth cardiac cycle out of n consecutive cardiac cycles; training a classifier based on the multiple training samples and the category corresponding to each training sample to obtain a trained classifier; training a first recurrent neural network based on training samples of the category of normal heart rate to obtain a trained first recurrent neural network; and training a second recurrent neural network based on training samples of the category of abnormal heart rate to obtain a trained second recurrent neural network, wherein the loss function of the first recurrent neural network and the second recurrent neural network is the root mean square error function.

[0016] In one possible implementation of the second aspect, the intra-aortic balloon counterpulsation system further includes a blood pressure monitoring module and a display module. The blood pressure monitoring module is electrically connected to the processor, and the display module is also electrically connected to the processor. After the processor controls the motor to operate according to control commands to adjust the internal pressure of the cylinder and complete the contraction and expansion of the balloon, the blood pressure monitoring module is used to: acquire the arterial blood pressure value of the target user and send the target user's arterial blood pressure value to the processor. The processor is also used to: generate a first alarm message if the target user's arterial blood pressure value is not within a preset blood pressure threshold range. The display module is used to: display the first alarm message, which is used to indicate that the target user's arterial blood pressure value is abnormal.

[0017] In one possible implementation of the second aspect, the intra-aortic balloon counterpulsation system further includes an electronic pressure relief valve and multiple pressure sensors. The electronic pressure relief valve and multiple pressure sensors are disposed in the air passage between the cylinder and the balloon, and are electrically connected to a processor. The pressure sensors are used to: acquire the pressure value inside the balloon and send the pressure value inside the balloon to the processor; the processor is also used to: control the electronic pressure relief valve to open when the pressure value inside the balloon is greater than a preset pressure threshold; and generate a second alarm message when the difference between multiple pressure values ​​acquired by multiple sensors between the cylinder and the balloon exceeds a preset pressure difference; the display module is also used to: display the second alarm message, which is used to indicate that the pressure sensors are abnormal; the intra-aortic balloon counterpulsation system also includes a mechanical pressure relief valve, which is disposed in the air passage between the cylinder and the balloon, and is used to open when the pressure value inside the balloon is greater than a preset pressure threshold.

[0018] Understandably, the beneficial effects that the system of the second aspect described above can achieve can be referenced from the beneficial effects of the first aspect and any of its possible design methods, which will not be repeated here. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of an intra-aortic balloon counterpulsation system according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of a neural network model according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of another intra-aortic balloon counterpulsation system as shown in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the structure of another intra-aortic balloon counterpulsation system as shown in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of another intra-aortic balloon counterpulsation system as shown in an embodiment of the present invention;

[0024] Figure 6 This is a schematic flowchart illustrating a control method for an intra-aortic balloon counterpulsation system according to an embodiment of the present invention;

[0025] Figure 7 This is a schematic flowchart illustrating another control method for an intra-aortic balloon counterpulsation system according to an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0027] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0028] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0029] The intra-aortic balloon pump (IABP) system is a key component in interventional treatment of cardiovascular diseases. The IABP system assists the heart by controlling the inflation and deflation of a balloon, thereby controlling blood flow through the user's aorta through counterpulsation.

[0030] Currently, IABP systems assist the heart by using electrocardiogram (ECG) triggering. ECG triggering can be understood as synchronizing the inflation and deflation of the IABP bladder based on the user's cardiac electrophysiological activity. During each cardiac cycle, a series of ECG signals are generated. By monitoring these signals, the IABP system can accurately identify the systolic and diastolic phases of the heart and control the inflation and deflation of the IABP bladder accordingly. However, this ECG triggering method has certain drawbacks. In cases of arrhythmia or poor ECG signal quality, the triggering method may fail to accurately identify the user's cardiac cycle, leading to a desynchronization between the bladder inflation / deflation process and the user's cardiac activity. Furthermore, ECG signals can be affected by electromagnetic interference, causing signal distortion or false triggering, which can lead to IABP system control failure. Therefore, the IABP system suffers from issues of poor safety and effectiveness.

[0031] Therefore, there is an urgent need for an inflation control method and system for the aortic balloon counterpulsation (IABP) balloon, which can quickly and accurately determine the user's cardiac cycle when the user has arrhythmia or poor ECG signal quality, and then control the inflation and deflation of the balloon of the IABP system based on the cardiac cycle, thereby improving the safety and effectiveness of the IABP system.

[0032] In view of this, embodiments of the present invention provide a control method for an intra-aortic balloon counterpulsation (IACP) system, applied to an ICP system. The ICP system includes a gas cylinder, a pressure regulator, a solenoid valve, a processor, a motor, a cylinder, an air bag, and an electrocardiogram (ECG) monitoring module. The gas cylinder, cylinder, and air bag are connected via a gas path. The solenoid valve controls the gas from the gas cylinder to enter the air bag and cylinder. The pressure regulator is located between the gas cylinder and the solenoid valve and controls the gas pressure output from the gas cylinder within a preset gas pressure threshold range. The processor is electrically connected to the motor and the ECG monitoring module, and the motor is connected to the cylinder. The method includes: the ECG monitoring module acquiring the ECG signal of the target user and sending it to the processor. The processor acquires the target user's ECG signal; if the amplitude of the target user's ECG signal is less than a preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than a preset waveform quality coefficient, the processor obtains the target user's historical ECG monitoring data, which includes ECG signals corresponding to multiple cardiac cycles of the target user within the target time period; the processor inputs the historical ECG monitoring data into a pre-trained neural network model to predict the target user's target cardiac cycle; the processor generates control commands based on the target user's target cardiac cycle; the processor controls the motor to operate according to the control commands, so that the motor adjusts the internal pressure of the cylinder to complete the contraction and expansion of the airbag.

[0033] The method provided in this invention, through a pre-trained neural network model, can quickly and accurately determine the target cardiac cycle of a target user based on historical ECG monitoring data when the user experiences arrhythmia or poor ECG signal quality. This allows for the inflation and deflation of the IABP system's cuff based on the target cardiac cycle. Compared to ECG-triggered control methods in related technologies, this method effectively improves the safety and effectiveness of the IABP system and reduces user risk. Furthermore, by incorporating a pressure regulator, the method ensures that the gas output from the cylinder is controlled within a preset gas pressure threshold range, enabling precise control of the gas volume and thus more accurate control of the cuff's contraction and expansion. Finally, by incorporating a mechanical pressure relief valve, the method provides accurate early warning of the internal pressure value of the cuff in case of electronic pressure relief valve failure, preventing unnecessary danger to the user.

[0034] In some embodiments, the control method of the intra-aortic balloon counterpulsation system provided in this invention can be executed by the intra-aortic balloon counterpulsation system 100 (hereinafter referred to as the counterpulsation system 100). See also Figure 1 , Figure 1 This is a schematic diagram of an intra-aortic balloon counterpulsation system according to an embodiment of the present invention. The counterpulsation system 100 includes a gas cylinder 110, a pressure regulator 120, a solenoid valve 130, a processor 140, a motor 150, a cylinder 160, an air bag 170, and an electrocardiogram monitoring module 180. The gas cylinder 110, cylinder 160, and air bag 170 are connected by a gas path. The solenoid valve 130 controls the gas in the gas cylinder 110 to enter the air bag 170 and cylinder 160. The pressure regulator 120 is disposed between the gas cylinder 110 and the solenoid valve 130, and is used to control the gas pressure output from the gas cylinder 110 within a preset gas pressure threshold range. The processor 140 is electrically connected to the motor 150 and the electrocardiogram monitoring module 180 respectively. The motor 150 is connected to the cylinder 160. Figure 1 The dashed lines in the diagram represent gas passages, while the solid lines represent electrical connections.

[0035] The ECG monitoring module 180 is used to acquire the ECG signal of the target user and send the ECG signal of the target user to the processor 140. The processor 140 is used to: acquire historical ECG monitoring data of the target user when the amplitude of the target user's ECG signal is less than a preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than a preset waveform quality coefficient; acquire historical ECG monitoring data including ECG signals corresponding to multiple cardiac cycles of the target user within a target time period; input the historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user; generate control commands based on the target cardiac cycle of the target user; and control the motor 150 to operate according to the control commands, so that the motor 150 adjusts the internal pressure of the cylinder 160 to complete the contraction and expansion of the airbag 170.

[0036] Among them, the target user's cardiac cycle is a time series consisting of the time interval between two consecutive heartbeats of the target user.

[0037] For example, processor 140 may include one or more processing cores. Processor 140 connects to various parts within counterpulsation system 100 using various interfaces and lines, and performs various functions of counterpulsation system 100 and processes data by running or executing instructions, programs, code sets, or instruction sets, and by calling data. Optionally, processor 140 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0038] For details, see Figure 2 , Figure 2This is a schematic diagram of a neural network model according to an embodiment of the present invention. The neural network model 200 includes a classifier 210, a first recurrent neural network 220, and a second recurrent neural network 230. The classifier 210 is connected to both the first recurrent neural network 220 and the second recurrent neural network 230. The classifier 210 is used to determine the category corresponding to historical ECG monitoring data, which includes normal heart rate or abnormal heart rate. It is also used to send historical ECG monitoring data of the category of normal heart rate to the first recurrent neural network 220 and to send historical ECG monitoring data of the category of abnormal heart rate to the second recurrent neural network 230. The first recurrent neural network 220 is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data of the category of normal heart rate. The second recurrent neural network 230 is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data of the category of abnormal heart rate.

[0039] For example, the first and second recurrent neural networks are Long Short-Term Memory (LSTM) networks. LSTM networks are a type of recurrent neural network used to process time-series data. They can capture long-term dependencies and are suitable for handling long-term memory (long-term dependencies) problems, while avoiding the vanishing and exploding gradient problems common in traditional recurrent neural networks during training. LSTM networks accurately predict time-series data through memory units and gating mechanisms.

[0040] In some embodiments, before the processor 140 inputs historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user, the processor 140 is specifically configured to: acquire training data, which includes ECG signals corresponding to multiple cardiac cycles of each user among multiple users, and a category corresponding to each user; perform sampling processing on the training data to obtain multiple training samples and a category corresponding to each training sample, wherein each training sample includes feature data and label data, the feature data being the ECG signals of the first n-1 cardiac cycles out of n consecutive cardiac cycles of any user, and the label data being... The data consists of the electrocardiogram (ECG) signal of the nth ECG cycle out of a series of n consecutive ECG cycles. A classifier 210 is trained based on multiple training samples and the corresponding category of each training sample, resulting in a trained classifier. A first recurrent neural network 220 is trained based on training samples classified as having a normal heart rate, resulting in a trained first recurrent neural network 220. A second recurrent neural network 230 is trained based on training samples classified as having an abnormal heart rate, resulting in a trained second recurrent neural network 230. The loss function for both the first and second recurrent neural networks 220 is the root mean square error function.

[0041] The following explanation uses a first recurrent neural network as an example to illustrate the training process of the first and second recurrent neural networks. When the first recurrent neural network is an LSTM, its training process includes a forward propagation phase and a backward propagation phase. In the forward propagation phase, the LSTM processes the input training samples step-by-step according to time steps and updates the model parameters (weights and biases). In the backward propagation phase, the LSTM calculates the gradient of the loss function with respect to each weight and bias using the chain rule, and uses these gradients to update the weights and biases included in the model parameters.

[0042] It should be understood that the system provided in this embodiment of the invention determines the category corresponding to the historical ECG monitoring data of the target user by setting a classifier, and then predicts the target cardiac cycle corresponding to the historical ECG monitoring data of different categories based on different recurrent neural networks, which can effectively reduce the complexity of the neural network model. This reduces the computational resources required to deploy the neural network model. Furthermore, since a recurrent neural network only needs to be trained on training samples of one category, the training time of the neural network model can be reduced, thereby improving the model training efficiency.

[0043] In one possible implementation, see Figure 3 The counterpulsation system 100 also includes a blood pressure monitoring module 190 and a display module 191. The blood pressure monitoring module 190 is electrically connected to the processor 140, and the display module 191 is electrically connected to the processor 140. After the processor 140 controls the motor 150 to operate according to control commands to adjust the internal pressure of the cylinder 160 and complete the contraction and expansion of the airbag 170, the blood pressure monitoring module 190 is used to: acquire the arterial blood pressure value of the target user and send the arterial blood pressure value of the target user to the processor 140. The processor 140 is also used to: generate a first alarm message if the arterial blood pressure value of the target user is not within the preset blood pressure threshold range. The display module 191 is used to: display the first alarm message, which is used to indicate that the arterial blood pressure value of the target user is abnormal.

[0044] The system provided in this embodiment of the invention monitors the arterial blood pressure of the target user. When the processor 140 controls the motor 150 to operate according to control instructions to adjust the internal pressure of the cylinder 160 and complete the contraction and expansion of the airbag 170, the system can monitor the life and health of the target user in real time, avoid unnecessary danger to the target user, and improve the safety of the system.

[0045] In some embodiments, see Figure 4The counter-pulsation system 100 also includes an electronic pressure relief valve 192 and multiple pressure sensors 193. The electronic pressure relief valve 192 and multiple pressure sensors 193 are disposed in the air passage between the cylinder 160 and the airbag 170. The electronic pressure relief valve 192 and multiple pressure sensors 193 are electrically connected to the processor 140. The pressure sensors 193 are used to: acquire the pressure value inside the airbag 170 and send the pressure value inside the airbag 170 to the processor 140. The processor 140 is also used to: control the electronic pressure relief valve 192 to open when the pressure value inside the airbag 170 is greater than a preset pressure threshold.

[0046] The system provided in this embodiment of the invention can quickly and accurately identify faulty pressure sensors by comparing the pressure values ​​obtained by multiple pressure sensors. For example, if the data from two different pressure sensors are too different, it indicates that one of the pressure sensors is faulty. The system can then prompt the operator to replace the pressure sensor in a timely manner through a second alarm message, which can effectively ensure the stable detection of air pressure in the air circuit.

[0047] The processor 140 is also configured to: generate a second alarm message when the difference between multiple pressure values ​​between the cylinder and the airbag acquired by multiple sensors exceeds a preset pressure difference; the display module is also configured to: display the second alarm message, which is used to indicate that the pressure sensor is abnormal;

[0048] The system provided in this embodiment of the invention can quickly and accurately identify faulty pressure sensors by comparing the pressure values ​​obtained by multiple pressure sensors. For example, if the data from two different pressure sensors are too different, it indicates that one of the pressure sensors is faulty. The system can then prompt the operator to replace the pressure sensor in a timely manner through a second alarm message, which can effectively ensure the stable detection of air pressure in the air circuit.

[0049] Further, see Figure 5 The counter-pulse system 100 also includes a mechanical pressure relief valve 195, which is disposed in the air passage between the cylinder 160 and the airbag 170. The mechanical pressure relief valve 195 is used to open when the pressure value inside the airbag 170 is greater than a preset pressure threshold.

[0050] The system provided in this embodiment of the invention, by setting a mechanical pressure relief valve, can automatically open if the pressure inside the airbag becomes too high and out of control due to system crashes or air circuit malfunctions. This prevents the airbag from being over-inflated or bursting, thus fully ensuring the user's safety.

[0051] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the counterpulsation system 100. In other embodiments of the present invention, the counterpulsation system 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0052] The control method of the intra-aortic balloon counterpulsation system provided in the embodiments of the present invention will be described below with reference to the accompanying drawings.

[0053] Figure 6 This is a flowchart illustrating a control method for an intra-aortic balloon counterpulsation system provided in an embodiment of the present invention. Optionally, this method can be executed by the aforementioned counterpulsation system 100. The method may include the following steps:

[0054] S1. The ECG monitoring module acquires the target user's ECG signal and sends the target user's ECG signal to the processor.

[0055] S2. When the amplitude of the target user's ECG signal is less than the preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than the preset waveform quality coefficient, the processor acquires the target user's historical ECG monitoring data.

[0056] Specifically, historical ECG monitoring data includes ECG signals corresponding to multiple cardiac cycles of the target user within the target time period.

[0057] S3. The processor inputs historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user.

[0058] In one possible implementation, the neural network model includes a classifier, a first recurrent neural network, and a second recurrent neural network, with the classifier connected to both the first and second recurrent neural networks. The classifier is used to determine the category corresponding to the historical ECG monitoring data, which includes normal heart rate or abnormal heart rate. It is also used to send historical ECG monitoring data categorized as normal heart rate to the first recurrent neural network and to send historical ECG monitoring data categorized as abnormal heart rate to the second recurrent neural network. The first recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as normal heart rate. The second recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as abnormal heart rate.

[0059] In some embodiments, see Figure 7 Prior to S3 above, the method provided in this embodiment of the invention further includes the following steps:

[0060] S31. The processor acquires training data, which includes electrocardiogram signals corresponding to multiple cardiac cycles for each user among multiple users, as well as the category corresponding to each user;

[0061] S32. The processor samples the training data to obtain multiple training samples and the category corresponding to each training sample.

[0062] Each training sample includes feature data and label data. The feature data is the ECG signal of the first n-1 ECG cycles out of n consecutive ECG cycles of any user, and the label data is the ECG signal of the nth ECG cycle out of n consecutive ECG cycles.

[0063] S33. The processor trains the classifier based on multiple training samples and the category corresponding to each training sample to obtain the trained classifier.

[0064] S34. The processor trains the first recurrent neural network based on training samples of the category of normal heart rate to obtain the first recurrent neural network after training; the processor trains the second recurrent neural network based on training samples of the category of abnormal heart rate to obtain the second recurrent neural network after training.

[0065] The loss function for both the first and second recurrent neural networks is the root mean square error function.

[0066] S4. The processor generates control instructions based on the target user's target heart rate cycle.

[0067] In one possible implementation, the above S4 specifically includes the following steps:

[0068] The processor determines the average value of multiple cardiac cycles included in the target user's historical ECG monitoring data;

[0069] If the difference between the target cardiac cycle of the target user and the average of multiple cardiac cycles is less than a preset threshold, the processor generates control instructions based on the target cardiac cycle of the target user.

[0070] If the difference between the target cardiac cycle of the target user and the average of multiple cardiac cycles is greater than or equal to a preset difference, the processor determines the average of multiple cardiac cycles as the target cardiac cycle of the target user and generates control instructions based on the target cardiac cycle of the target user.

[0071] The method provided in this invention verifies the target cardiac cycle predicted by the neural network model by using the average value of multiple cardiac cycles included in the historical electrocardiogram monitoring data of the target user. After successful verification, control commands are generated based on the target cardiac cycle of the target user, which can effectively improve the safety of the counterpulsation system.

[0072] S5. The processor controls the motor to run according to the control instructions, so that the motor adjusts the internal pressure of the cylinder to complete the contraction and expansion of the airbag.

[0073] As can be seen from S1-S5 above, the method provided by the embodiments of the present invention, through a pre-trained neural network model, can quickly and accurately determine the target cardiac cycle of the target user based on the target user's historical ECG monitoring data when the target user has arrhythmia or poor ECG signal quality. Then, it can inflate and deflate the airbag of the IABP system according to the target cardiac cycle. Compared with the control method triggered by ECG in related technologies, the method provided by the embodiments of the present invention can effectively improve the safety and effectiveness of the IABP system and reduce the user's usage risk.

[0074] In some embodiments, after S5 above, the method provided by the present invention further includes the following steps: the blood pressure monitoring module acquires the arterial blood pressure value of the target user and sends the arterial blood pressure value of the target user to the processor; the processor generates a first alarm message when the arterial blood pressure value of the target user is not within a preset blood pressure threshold range; the display module displays the first alarm message, which is used to indicate that the arterial blood pressure value of the target user is abnormal.

[0075] In other embodiments, the method provided by the present invention further includes the following steps: a pressure sensor acquires the pressure value inside the airbag and sends the pressure value inside the airbag to a processor; when the pressure value inside the airbag is greater than a preset pressure threshold, the processor controls the electronic pressure relief valve to open.

[0076] Optionally, the method provided in this embodiment of the invention further includes the following steps: when the difference between multiple pressure values ​​between the cylinder and the airbag obtained by multiple sensors exceeds a preset pressure difference, the processor generates a second alarm message; the display module displays the second alarm message, which is used to indicate that the pressure sensor is abnormal.

[0077] It should be understood that a detailed description of the above-mentioned optional methods can be found in the foregoing system embodiments, and will not be repeated here. Furthermore, explanations of any of the control methods provided above, as well as descriptions of their beneficial effects, can be found in the corresponding system embodiments described above, and will not be repeated here.

[0078] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0079] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the prediction system 100 described above, and one or more ports. Optionally, the functions supported by this chip are as described above, and will not be repeated here.

[0080] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0081] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0082] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0083] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An intra-aortic balloon counterpulsation system, characterized in that, The intra-aortic balloon counterpulsation system includes a gas cylinder, a pressure regulator, a solenoid valve, a processor, a motor, a cylinder, an air bag, and an electrocardiogram (ECG) monitoring module. The gas cylinder, cylinder, and air bag are connected via a gas path. The solenoid valve controls the flow of gas from the gas cylinder into the air bag and cylinder. The pressure regulator is located between the gas cylinder and the solenoid valve and controls the gas pressure output from the gas cylinder within a preset gas pressure threshold range. The processor is electrically connected to the motor and the ECG monitoring module, and the motor is connected to the cylinder. The electrocardiogram monitoring module is used for: Acquire the target user's electrocardiogram (ECG) signal and send the target user's ECG signal to the processor; The processor is used for: If the amplitude of the target user's electrocardiogram (ECG) signal is less than a preset amplitude or the waveform quality coefficient corresponding to the target user's ECG signal is less than a preset waveform quality coefficient, historical ECG monitoring data of the target user is acquired. The historical ECG monitoring data includes ECG signals corresponding to multiple cardiac cycles of the target user within a target time period. The historical ECG monitoring data is input into a pre-trained neural network model to predict the target cardiac cycle of the target user. Control commands are generated based on the target cardiac cycle of the target user. The motor is controlled to operate according to the control commands, so that the motor adjusts the internal pressure of the cylinder to complete the contraction and expansion of the airbag. The neural network model includes a classifier, a first recurrent neural network, and a second recurrent neural network. The classifier is connected to both the first and second recurrent neural networks. The classifier is used to determine the category corresponding to the historical ECG monitoring data, which includes normal heart rhythm or abnormal heart rate. It is also used to send historical ECG monitoring data categorized as normal heart rate to the first recurrent neural network and to send historical ECG monitoring data categorized as abnormal heart rate to the second recurrent neural network. The first recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as normal heart rate. The second recurrent neural network is used to output the corresponding target cardiac cycle based on the input historical ECG monitoring data categorized as abnormal heart rate.

2. The system according to claim 1, characterized in that, Before the processor inputs the historical ECG monitoring data into a pre-trained neural network model to predict the target cardiac cycle of the target user, the processor is specifically used for: Acquire training data, which includes electrocardiogram signals corresponding to multiple cardiac cycles for each user among multiple users, and the category corresponding to each user; The training data is sampled to obtain multiple training samples and the category corresponding to each training sample. Each training sample includes feature data and label data. The feature data is the ECG signal of the first n-1 ECG cycles in the ECG signal of any user in the continuous n ECG cycles. The label data is the ECG signal of the nth ECG cycle in the continuous n ECG cycles. The classifier is trained based on multiple training samples and the category corresponding to each training sample to obtain a trained classifier. The first recurrent neural network is trained based on training samples of the category of normal heart rate, and the trained first recurrent neural network is obtained. The processor trains the second recurrent neural network based on training samples categorized as heart rate abnormalities, resulting in a trained second recurrent neural network. The loss function for both the first and second recurrent neural networks is the root mean square error function.

3. The system according to claim 2, characterized in that, The intra-aortic balloon counterpulsation system also includes a blood pressure monitoring module and a display module. The blood pressure monitoring module is electrically connected to the processor, and the display module is electrically connected to the processor. After the processor controls the motor to operate according to the control instructions to adjust the internal pressure of the cylinder and complete the contraction and expansion of the airbag, the blood pressure monitoring module is used to: acquire the arterial blood pressure value of the target user and send the arterial blood pressure value of the target user to the processor; The processor is also configured to: generate a first alarm message when the target user's arterial blood pressure value is not within a preset blood pressure threshold range; The display module is used to display the first alarm information, which is used to indicate that the target user's arterial blood pressure value is abnormal.

4. The system according to claim 3, characterized in that, The intra-aortic balloon counterpulsation system also includes an electronic pressure relief valve and multiple pressure sensors. The electronic pressure relief valve and the multiple pressure sensors are disposed in the air passage between the cylinder and the balloon, and the electronic pressure relief valve and the multiple pressure sensors are electrically connected to the processor. The pressure sensor is used to: acquire the pressure value inside the airbag and send the pressure value inside the airbag to the processor; The processor is also used for: When the pressure inside the airbag exceeds a preset pressure threshold, the electronic pressure relief valve is opened. If the difference between multiple pressure values ​​between the cylinder and the airbag obtained by the multiple sensors exceeds a preset pressure difference, a second alarm message is generated. The display module is also used to: display the second alarm information, which is used to indicate that the pressure sensor is abnormal; The intra-aortic balloon counterpulsation system also includes a mechanical pressure relief valve, which is disposed in the air passage between the cylinder and the balloon. The mechanical pressure relief valve is used to open when the pressure value inside the balloon is greater than a preset pressure threshold.

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