A dynamic pressure adaptive arterial puncture closure system and method

Abstract

This invention discloses a dynamic pressure-adaptive arterial puncture closure system and method, relating to the field of medical device technology. The system includes: a pressure sensing module for acquiring pressure distribution information at different circumferential locations within the blood vessel and sensing pulsating pressure signals in real time; an intelligent control module electrically connected to the pressure sensing module for receiving and processing the pulsating pressure signals and generating dynamic closure control commands according to a preset adaptive algorithm; and a mechanical execution module electrically connected to the intelligent control module for receiving the dynamic closure control commands and driving the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site. This invention can adapt to the dynamic changes in the patient's blood pressure in real time and automatically. Through a dynamic pressure adaptive mechanism, it achieves a fundamental shift from "static compression" to "dynamic adaptive compression," minimizing vascular wall damage while ensuring hemostasis and significantly reducing the risk of complications.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a dynamic pressure adaptive arterial puncture closure system and method. Background Technology

[0002] Percutaneous arterial puncture is a routine approach for cardiovascular interventional diagnosis and treatment. After the procedure, effective hemostasis at the puncture site is a key step in preventing complications such as bleeding, hematoma, and pseudoaneurysm.

[0003] Currently, commonly used closure techniques in clinical practice mainly include manual compression, mechanical compression devices, and various vascular closure devices. Among existing technologies, advanced vascular closure devices (such as suture-type and occlusion-type closure devices) can achieve rapid hemostasis, but their working principle is mostly a "one-time" or "pre-set" mechanical action. The closure force is set once during implantation and cannot be dynamically adjusted according to the patient's real-time blood pressure fluctuations in the postoperative stage.

[0004] In real-world clinical settings, a patient's blood pressure fluctuates dynamically due to factors such as pain, emotions, and drug responses. With traditional fixed-force closure methods, when blood pressure rises, insufficient closure force may lead to delayed bleeding or hematoma; conversely, when blood pressure drops, excessive closure force may over-compress the blood vessel, increasing the risk of vascular stenosis, occlusion, or distal ischemia. Furthermore, current technologies lack the ability to monitor intravascular pressure in real-time and directly, making it impossible for operators to quantify the closure effect. They rely primarily on experience and external observation, which necessitates improvements in accuracy and safety.

[0005] Therefore, proposing a dynamic pressure adaptive arterial puncture closure system and method to overcome the difficulties of the existing technology is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a dynamic pressure adaptive arterial puncture closure system and method that can adapt to the dynamic changes in the patient's blood pressure in real time and automatically. Through the dynamic pressure adaptive mechanism, it realizes a fundamental transformation from "static compression" to "dynamic adaptive compression", minimizing vascular wall damage while ensuring hemostasis and significantly reducing the risk of complications.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic pressure-adaptive arterial puncture closure system, comprising: The pressure sensing module is used to acquire pressure distribution information at different circumferential positions inside the blood vessel and to sense pulsating pressure signals in real time. The intelligent control module, electrically connected to the pressure sensing module, is used to receive and process pulsating pressure signals and generate dynamic closure control commands according to a preset adaptive algorithm. The mechanical actuation module, electrically connected to the intelligent control module, is used to receive dynamic closure control commands and drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

[0008] The aforementioned system, optionally, includes a pressure sensing module integrated into the guide sheath distal to the closure element or near the puncture point, comprising: a miniature pressure sensor array and a multiplexer.

[0009] A miniature pressure sensor array is used to acquire pressure distribution information at different circumferential locations on the inner wall of blood vessels; The multiplexer, integrated on the flexible circuit board where the sensor array is located, is used to acquire the output signals of each sensor in the array at high speed in a time-division multiplexing manner to obtain the pulsating pressure signal.

[0010] In the aforementioned system, optionally, each sensor in the miniature pressure sensor array is connected to a multiplexer via micrometer-scale metal traces; The multiplexer transmits pulsating pressure signals from inside the blood vessel to outside the body via a catheter lumen through a flexible cable that integrates multiple micro-wires.

[0011] The above-mentioned system may optionally include an intelligent control module comprising: a signal processing unit, a situational awareness unit, and a decision-making unit connected in sequence; The signal processing unit is used to filter out noise in the pulsating pressure signal to obtain a clean intravascular pressure waveform and pressure distribution map; Situational awareness unit: Extracts intravascular pressure waveform feature values, including systolic pressure, diastolic pressure, mean pressure, and pressure waveform morphology feature values; analyzes pressure distribution map, calculates key feature parameters of pressure distribution matrix, including pressure center coordinates, distribution uniformity index, and maximum pressure gradient; Decision unit: Embedded with a machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force, it predicts and outputs the optimal closure force in real time based on the feature values ​​of intravascular pressure waveform and key feature parameters of pressure distribution matrix, and generates dynamic closure control commands.

[0012] The aforementioned system, optionally, uses a machine learning model based on the mapping relationship between blood pressure waveform features and optimal closing force, which is a hybrid architecture of a fuzzy proportional-integral-differential controller and a lightweight neural network model. A neural network model is used to output a suggested target closure force value based on the feature values ​​of the intravascular pressure waveform and the key feature parameters of the pressure distribution matrix. A fuzzy proportional-integral-derivative controller is used to generate dynamic closure control commands based on the deviation between the target closure force recommendation value and the actual closure force feedback value. Based on the pressure center coordinates and distribution uniformity index, when it is determined that there is eccentric compression, attitude adjustment commands are preferentially generated to adjust the attitude of the closed element.

[0013] The above system, optionally, includes a mechanical actuation module comprising: a miniature linear actuator, a transmission mechanism, and a force feedback unit connected in sequence; A miniature linear actuator that generates linear displacement based on a dynamic closure control command; The transmission mechanism converts linear displacement into radial contraction or expansion motion of a closed element; The force feedback unit monitors the actual applied radial closing force in real time and forms a closed loop to feed back to the intelligent control module.

[0014] In the aforementioned system, optionally, the closure element is a radially expandable mesh scaffold structure or a cuff structure, the surface of which is coated with a procoagulant biological coating.

[0015] A dynamic pressure adaptive arterial puncture closure method, applied to any of the above-described dynamic pressure adaptive arterial puncture closure systems, comprising: S1. Acquire real-time pulsating pressure signal and circumferential pressure distribution information from the pressure sensing module; S2. Filter out noise in the pulsating pressure signal to obtain a pure intravascular pressure waveform and pressure distribution map, extract the feature values ​​of the intravascular pressure waveform, and calculate the key feature parameters of the pressure distribution matrix. S3. Determine whether there is eccentric compression based on the key feature parameters of the pressure distribution matrix. If it exists, generate attitude adjustment commands first. S4. Input the intravascular pressure waveform feature values ​​into a pre-trained machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force. The model predicts and outputs the expected optimal closure force parameters that match the current blood pressure status in real time. S5. Generate dynamic closure control commands based on the difference between the desired optimal closure force parameters and the actual closure force parameters from the force feedback unit; S6. According to the dynamic closure control command, drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

[0016] As can be seen from the above technical solution, compared with the prior art, the present invention provides a dynamic pressure adaptive arterial puncture closure system and method, which has the following beneficial effects: This invention monitors intravascular pressure in real time and dynamically adjusts the closure force, maintaining the pressure within the optimal range of "sufficient to stop bleeding but not excessive," fundamentally avoiding incomplete closure (bleeding) or excessive compression (vascular damage) caused by blood pressure changes, significantly reducing the incidence of related complications. The system automates the complex pressure judgment and force control process, reducing reliance on the operator's personal experience and feel, shortening the learning curve, and providing a more consistent and reliable closure effect. The system can record and display the pressure-closure force change curve and pressure distribution map throughout the entire postoperative period, providing doctors with objective and quantitative evaluation of the closure effect, facilitating more refined postoperative management. Through closed-loop control, the closure force is optimized to the minimum necessary range, and it has the ability to identify and correct eccentric compression, avoiding damage to the vascular wall from continuous or localized high pressure, potentially reducing the risk of long-term complications such as vascular stenosis and thrombosis. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 A structural diagram of a dynamic pressure adaptive arterial puncture closure system provided by the present invention; Figure 2 A structural diagram of an intelligent control module in a dynamic pressure adaptive arterial puncture closure system provided by the present invention; Figure 3 A structural diagram of the mechanical execution module in a dynamic pressure adaptive arterial puncture closure system provided by the present invention; Figure 4 The flowchart illustrates a dynamic pressure-adaptive arterial puncture closure method provided by this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Reference Figure 1 As shown, this invention discloses a dynamic pressure adaptive arterial puncture closure system, comprising: The pressure sensing module is used to acquire pressure distribution information at different circumferential positions inside the blood vessel and to sense pulsating pressure signals in real time. The intelligent control module, electrically connected to the pressure sensing module, is used to receive and process pulsating pressure signals and generate dynamic closure control commands according to a preset adaptive algorithm. The mechanical actuation module, electrically connected to the intelligent control module, is used to receive dynamic closure control commands and drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

[0021] Furthermore, the pressure sensing module, integrated on the guide sheath distal to the closure element or near the puncture point, includes: a miniature pressure sensor array and a multiplexer.

[0022] A miniature pressure sensor array is used to acquire pressure distribution information at different circumferential locations on the inner wall of blood vessels; The multiplexer, integrated on the flexible circuit board where the sensor array is located, is used to acquire the output signals of each sensor in the array at high speed in a time-division multiplexing manner to obtain the pulsating pressure signal.

[0023] Furthermore, each sensor in the miniature pressure sensor array is connected to a multiplexer via micrometer-scale metal traces; The multiplexer transmits pulsating pressure signals from inside the blood vessel to outside the body via a catheter lumen through a flexible cable that integrates multiple micro-wires.

[0024] Furthermore, the pressure sensing module is the system's "sensory organ," and its detailed structure is as follows: Core sensing unit: Miniaturized microelectromechanical system piezoresistive or capacitive pressure sensors are arranged in a matrix (such as 5×5 or 8×8) on a flexible substrate. Each sensing unit is approximately 200×200μm² in size and 300μm apart. Flexible carrier and packaging: The sensor is manufactured or attached to an ultra-thin flexible circuit board made of polyimide or medical silicone. The circuit board is pre-formed to match the curvature of the outer surface of the closed element and is seamlessly integrated. The surface is covered with a biocompatible elastic protective film. Multiplexer: Integrated on a flexible circuit board, it is used to acquire the output signals of each sensor in the array at high speed in a time-division multiplexing manner, and transmit the signals to the intelligent control module through a limited number of signal transmission lines, thus solving the contradiction between high-density sensing and miniaturization of medical devices. Signal pathway: The signal is delivered from inside the blood vessel to the outside through the catheter lumen via a flexible cable that integrates multiple micro-wires; Furthermore, when the closure element expands inside the blood vessel, the pressure of the blood vessel wall acts on the sensor, causing a slight deformation of the sensitive unit; the resistance value of the Wheatstone bridge inside the piezoresistive sensor changes, generating a voltage difference output proportional to the pressure; the multiplexer scans each sensor in the array at a high speed of thousands of times per second; the collected analog signals are pre-amplified and filtered before being transmitted to the intelligent control module, and the final output is a two-dimensional pressure data matrix that is updated over time, representing the pressure distribution information at different circumferential positions of the blood vessel wall.

[0025] Furthermore, refer to Figure 2 As shown, the intelligent control module includes: a signal processing unit, a situational awareness unit, and a decision-making unit connected in sequence; The signal processing unit is used to filter out noise in the pulsating pressure signal to obtain a clean intravascular pressure waveform and pressure distribution map; Situational awareness unit: Extracts intravascular pressure waveform feature values, including systolic pressure, diastolic pressure, mean pressure, and pressure waveform morphology feature values; analyzes pressure distribution map, calculates key feature parameters of pressure distribution matrix, including pressure center coordinates, distribution uniformity index, and maximum pressure gradient; Decision unit: Embedded with a machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force, it predicts and outputs the optimal closure force in real time based on the feature values ​​of intravascular pressure waveform and key feature parameters of pressure distribution matrix, and generates dynamic closure control commands.

[0026] Furthermore, the machine learning model based on the mapping relationship between blood pressure waveform features and optimal closing force is a hybrid architecture of fuzzy proportional-integral-differential controller and lightweight neural network model. A neural network model is used to output a suggested target closure force value based on the feature values ​​of the intravascular pressure waveform and the key feature parameters of the pressure distribution matrix. A fuzzy proportional-integral-derivative controller is used to generate dynamic closure control commands based on the deviation between the target closure force recommendation value and the actual closure force feedback value. Based on the pressure center coordinates and distribution uniformity index, when it is determined that there is eccentric compression, attitude adjustment commands are preferentially generated to adjust the attitude of the closed element.

[0027] Furthermore, the intelligent control module receives the raw pressure data stream and cleans it through a digital filter; it identifies the current blood pressure status and trend, and determines whether there is eccentric compression; if eccentric compression exists, it prioritizes generating posture adjustment commands (such as slightly rotating the catheter or adjusting the expansion shape); if the pressure distribution is normal, it inputs the characteristic parameters into the neural network model and outputs the target closing force; the fuzzy proportional-integral-derivative controller generates motor control commands based on the deviation between the target value and the actual value, and sends them to the mechanical execution module through a digital isolator and motor drive circuit.

[0028] Furthermore, refer to Figure 3 As shown, the mechanical actuation module includes: a miniature linear actuator, a transmission mechanism, and a force feedback unit connected in sequence; A miniature linear actuator that generates linear displacement based on a dynamic closure control command; The transmission mechanism converts linear displacement into radial contraction or expansion motion of the closed element; specifically: the rotation of the motor is converted into linear motion through a ball screw or miniature trapezoidal screw, and the screw nut is mechanically connected to the closed element inside the body through a push rod or traction line, thus converting the linear displacement into radial contraction or expansion motion of the closed element. The force feedback unit integrates a miniature strain gauge force sensor on the lead screw nut or push rod to monitor the actual applied radial closing force in real time and form a closed loop feedback to the intelligent control module. The force feedback unit continuously measures the actual output closing force and transmits it back to the intelligent control module in real time. The intelligent control module compares the actual output closing force with the target closing force. If there is a deviation, it dynamically corrects the motor command. This closed loop feedback runs continuously at a millisecond frequency until the actual closing force matches the target value, and dynamically maintains this state until the system issues a removal command.

[0029] Furthermore, the closure element is a radially expandable mesh scaffold structure or a cuff structure, the surface of which is coated with a procoagulant bio-coating.

[0030] Reference Figure 4 As shown, a dynamic pressure adaptive arterial puncture closure method, applied to any of the above-described dynamic pressure adaptive arterial puncture closure systems, includes: S1. Acquire real-time pulsating pressure signal and circumferential pressure distribution information from the pressure sensing module; S2. Filter out noise in the pulsating pressure signal to obtain a pure intravascular pressure waveform and pressure distribution map, extract the feature values ​​of the intravascular pressure waveform, and calculate the key feature parameters of the pressure distribution matrix. S3. Determine whether there is eccentric compression based on the key feature parameters of the pressure distribution matrix. If it exists, generate attitude adjustment commands first. S4. Input the intravascular pressure waveform feature values ​​into a pre-trained machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force. The model predicts and outputs the expected optimal closure force parameters that match the current blood pressure status in real time. S5. Generate dynamic closure control commands based on the difference between the desired optimal closure force parameters and the actual closure force parameters from the force feedback unit; S6. According to the dynamic closure control command, drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

[0031] In one specific embodiment, after the surgery is completed, a disposable closed catheter is inserted into the artery along the guidewire, so that the closure element with integrated sensor accurately crosses the puncture site and is located in the blood vessel. The system is activated, the closure element initially expands to contact the blood vessel wall, and the pressure sensor begins to transmit the initial pressure signal. The intelligent control module filters the raw pressure signal to extract a clear and stable arterial pressure waveform and pressure distribution map. First, it analyzes the pressure distribution to determine whether there is eccentric compression. If the distribution is normal, the current waveform feature value is input into the neural network model, and the model outputs the "target closing force F_target" corresponding to the current blood pressure state, which aims to achieve hemostasis and minimize lateral pressure. The F_target command is sent to the mechanical execution module, and the linear actuator starts to move. The diameter of the closing element is finely adjusted through the transmission mechanism to change its pressure on the blood vessel wall. The force feedback sensor integrated on the transmission mechanism continuously measures the actual output closing force F_actual and sends it back to the intelligent control module in real time. The intelligent control module compares F_target and F_actual, and dynamically corrects the instructions given to the actuator through a fuzzy proportional-integral-derivative controller until the error between the two approaches zero, thus achieving precise and stable control. During the subsequent monitoring period (e.g., 30-60 minutes), the system continues to run the above closed loop, dynamically adapting to any blood pressure fluctuations that the patient may experience. Once the preset safe hemostasis time is reached, the doctor can control the closure element to radially contract to its minimum size via instructions, and the catheter can then be safely withdrawn from the body.

[0032] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0033] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic pressure adaptive arterial puncture closure system, characterized in that, include: The pressure sensing module is used to acquire pressure distribution information at different circumferential positions inside the blood vessel and to sense pulsating pressure signals in real time. The intelligent control module, electrically connected to the pressure sensing module, is used to receive and process pulsating pressure signals and generate dynamic closure control commands according to a preset adaptive algorithm. The mechanical actuation module, electrically connected to the intelligent control module, is used to receive dynamic closure control commands and drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

2. The dynamic pressure adaptive arterial puncture closure system according to claim 1, characterized in that, The pressure sensing module, integrated into the guide sheath distal to the closure element or near the puncture point, includes: a miniature pressure sensor array and a multiplexer. A miniature pressure sensor array is used to acquire pressure distribution information at different circumferential locations on the inner wall of blood vessels; The multiplexer, integrated on the flexible circuit board where the sensor array is located, is used to acquire the output signals of each sensor in the array at high speed in a time-division multiplexing manner to obtain the pulsating pressure signal.

3. The dynamic pressure adaptive arterial puncture closure system according to claim 2, characterized in that, Each sensor in the miniature pressure sensor array is connected to a multiplexer via micrometer-scale metal traces; The multiplexer transmits pulsating pressure signals from inside the blood vessel to outside the body via a catheter lumen through a flexible cable that integrates multiple micro-wires.

4. The dynamic pressure adaptive arterial puncture closure system according to claim 3, characterized in that, The intelligent control module includes: a signal processing unit, a situational awareness unit, and a decision-making unit connected in sequence; The signal processing unit is used to filter out noise in the pulsating pressure signal to obtain a clean intravascular pressure waveform and pressure distribution map; Situational awareness unit: Extracts intravascular pressure waveform feature values, including systolic pressure, diastolic pressure, mean pressure, and pressure waveform morphology feature values; analyzes pressure distribution map, calculates key feature parameters of pressure distribution matrix, including pressure center coordinates, distribution uniformity index, and maximum pressure gradient; Decision unit: Embedded with a machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force, it predicts and outputs the optimal closure force in real time based on the feature values ​​of intravascular pressure waveform and key feature parameters of pressure distribution matrix, and generates dynamic closure control commands.

5. The dynamic pressure adaptive arterial puncture closure system according to claim 4, characterized in that, The machine learning model based on the mapping relationship between blood pressure waveform features and optimal closing force is a hybrid architecture of fuzzy proportional-integral-differential controller and lightweight neural network model. A neural network model is used to output a suggested target closure force value based on the feature values ​​of the intravascular pressure waveform and the key feature parameters of the pressure distribution matrix. A fuzzy proportional-integral-derivative controller is used to generate dynamic closure control commands based on the deviation between the target closure force recommendation value and the actual closure force feedback value. Based on the pressure center coordinates and distribution uniformity index, when it is determined that there is eccentric compression, attitude adjustment commands are preferentially generated to adjust the attitude of the closed element.

6. The dynamic pressure adaptive arterial puncture closure system according to claim 5, characterized in that, The mechanical actuation module includes: a miniature linear actuator, a transmission mechanism, and a force feedback unit connected in sequence; A miniature linear actuator that generates linear displacement based on a dynamic closure control command; The transmission mechanism converts linear displacement into radial contraction or expansion motion of a closed element; The force feedback unit monitors the actual applied radial closing force in real time and forms a closed loop to feed back to the intelligent control module.

7. The dynamic pressure adaptive arterial puncture closure system according to claim 6, characterized in that, The closure element is a radially expandable mesh scaffold structure or cuff structure, with its surface coated with a procoagulant biofilm coating.

8. A dynamic pressure adaptive arterial puncture closure method, applied to the dynamic pressure adaptive arterial puncture closure system according to any one of claims 1-7, comprising: S1. Acquire real-time pulsating pressure signal and circumferential pressure distribution information from the pressure sensing module; S2. Filter out noise in the pulsating pressure signal to obtain a pure intravascular pressure waveform and pressure distribution map, extract the feature values ​​of the intravascular pressure waveform, and calculate the key feature parameters of the pressure distribution matrix. S3. Determine whether there is eccentric compression based on the key feature parameters of the pressure distribution matrix. If it exists, generate attitude adjustment commands first. S4. Input the intravascular pressure waveform feature values ​​into a pre-trained machine learning model based on the mapping relationship between blood pressure waveform features and optimal closure force. The model predicts and outputs the expected optimal closure force parameters that match the current blood pressure status in real time. S5. Generate dynamic closure control commands based on the difference between the desired optimal closure force parameters and the actual closure force parameters from the force feedback unit; S6. According to the dynamic closure control command, drive the closure element to apply a dynamically adjustable radial closure force to the arterial puncture site.

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