Intracranial pressure real-time monitoring and drainage adaptive control optimization system

Abstract

The invention provides an intracranial pressure real-time monitoring and drainage adaptive control optimization system. The intracranial pressure real-time monitoring and drainage self-adaptive control optimization system comprises a hardware module, a software module and a mathematical model, the intracranial pressure is monitored in real time through a high-precision sensor, self-adaptive adjustment is carried out based on individual characteristics of a patient to optimize drainage control, and the hardware module comprises the high-precision pressure sensor used for monitoring the intracranial pressure in real time, and the software module is used for monitoring the intracranial pressure in real time; the drainage pipeline is connected into the patient valine and used for adjusting drainage of cerebrospinal fluid. By combining the high-precision pressure sensor, the artificial intelligence algorithm, the neural network and the fuzzy control strategy, the real-time response capability of the system to the intracranial pressure change is remarkably improved. The preferably adopted micro electro mechanical system sensor has the advantages of being high in precision and low in delay, intracranial pressure changes as small as 0.01 mmHg can be detected, and the drifting phenomenon of the sensor in long-term monitoring is reduced through the self-calibration function.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time intracranial pressure monitoring, and in particular to a real-time intracranial pressure monitoring and drainage adaptive control optimization system. Background Art

[0002] The real-time intracranial pressure monitoring and drainage adaptive control optimization system consists of core components such as pressure sensors, drainage lines, controllers, and actuators. The pressure sensor monitors intracranial pressure in real time and transmits data to the controller, which automatically adjusts the drainage volume based on the preset intracranial pressure threshold. The actuator controls the opening and closing of the valve or pump in the drainage line and the flow rate, so that the intracranial pressure remains within a relatively stable and safe range. Through this closed-loop control method, the system can quickly respond to changes in intracranial pressure and avoid the risk of brain damage or insufficient drainage due to excessive intracranial pressure.

[0003] Although the system can effectively monitor and regulate intracranial pressure, it has some flaws. The accuracy and sensitivity of the sensor are limited, and it may not be able to fully capture small pressure fluctuations, resulting in a delayed drainage response. The system's setting value for intracranial pressure depends on the doctor's experience and judgment, and it is difficult to accurately adjust it according to individual patient differences. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a real-time monitoring and drainage adaptive control optimization system for intracranial pressure, which solves the problem of delayed drainage response caused by limited accuracy and sensitivity of the sensor; and the problem that the setting value of intracranial pressure depends on the doctor's experience and judgment, and is difficult to accurately adjust according to individual differences of patients.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time intracranial pressure monitoring and drainage adaptive control optimization system, comprising:

[0006] Hardware modules, software modules and mathematical models monitor intracranial pressure in real time through high-precision sensors and make adaptive adjustments based on individual patient characteristics to optimize drainage control;

[0007] Its hardware modules include: high-precision pressure sensor for real-time monitoring of intracranial pressure,

[0008] Drainage tube, connected to the patient's anus, is used to regulate the drainage of cerebrospinal fluid.

[0009] Actuator, which controls the opening and closing of valves or pumps in the drainage pipeline and the flow rate,

[0010] A controller receives the intra-arterial pressure data from the sensor, calculates and executes drainage control instructions;

[0011] Among them, the software module is based on artificial intelligence algorithm, and establishes a personalized intra-artificial pressure threshold model by analyzing sensor data and the patient's physiological parameters. The personalized intra-artificial pressure threshold model includes a neural network and a fuzzy control algorithm, which is used to dynamically adjust the drainage volume. The specific formula is as follows:

[0012]

[0013] Where: Q drain is the instantaneous drainage volume, P ICP is the real-time monitoring of intracranial pressure, P set It is a personalized intracranial pressure setting value calculated based on the patient's individual characteristics and historical data. is the rate of change of intracranial pressure, K gain is the proportional gain coefficient, used to control the drainage reaction speed, K adjust It is an adaptive adjustment coefficient that dynamically adjusts the drainage strategy according to the patient's physiological parameters.

[0014] This formula realizes real-time monitoring of intracranial pressure and precise drainage regulation through the proportional-differential control method combined with neural network and fuzzy control strategy, effectively solves the drainage lag problem caused by limited sensor accuracy, and dynamically adjusts the drainage setting value based on the individual characteristics of the patient.

[0015] Preferably, the high-precision pressure sensor adopts micro-electromechanical system technology, has high-precision, low-latency measurement performance, and can sense intracranial pressure changes as small as 0.01 mmHg. The pressure sensor has a self-calibration function and can automatically adjust its sensitivity during long-term monitoring to reduce sensor drift.

[0016] Preferably, the drainage pipeline adopts an anti-clogging design and the inner wall is coated with antibacterial material to reduce the risk of pipeline blockage and infection during long-term use. The actuator is a miniature solenoid valve or an intelligent pump with a high-precision flow regulation function and can achieve continuous adjustable control of the flow rate according to controller instructions.

[0017] Preferably, the controller includes an artificial intelligence processing unit that can analyze the intracranial pressure data from the sensor in real time, and calculate the intracranial pressure setting value suitable for the patient through a neural network model in combination with the individual characteristics of the patient.

[0018] Preferably, the fuzzy control algorithm is used to process nonlinear changes in intracranial pressure. The controller can provide smooth drainage adjustment according to different rates and degrees of change to avoid excessive drainage or delayed response. The artificial intelligence processing unit uses a deep neural network model to predict the trend of intracranial pressure changes based on the patient's historical monitoring data and physiological parameters, and dynamically adjust the drainage set value.

[0019] Preferably, the controller transmits data with an external device through a wireless communication module to achieve remote monitoring, parameter adjustment and system diagnosis. The external device includes a doctor operation terminal. The doctor can view the patient's intracranial pressure monitoring data in real time through the terminal and remotely adjust the parameters of the drainage system according to the patient's condition.

[0020] Preferably, the regression model adopts a multivariate linear regression or nonlinear regression algorithm to dynamically adjust the set value of intracranial pressure according to the physiological differences of different patients. The system calculates the amplitude and phase of intracranial pressure fluctuations through a frequency domain analysis method, and further optimizes the drainage control strategy based on the statistical confidence interval to ensure the safety and effectiveness of drainage.

[0021] Preferably, the controller includes an alarm module, which automatically triggers an alarm when the intracranial pressure exceeds a preset safety threshold range, and notifies a doctor through the wireless communication module for processing. The alarm module can distinguish critical situations through different alarm levels according to the rapid changes in intracranial pressure or the trend of long-term deviation from the normal range, so as to facilitate timely intervention.

[0022] The present invention provides a real-time monitoring system for intracranial pressure and adaptive drainage control optimization, which has the following beneficial effects:

[0023] The present invention significantly improves the system's real-time response capability to changes in intracranial pressure by combining high-precision pressure sensors, artificial intelligence algorithms, neural networks, and fuzzy control strategies. The preferred micro-electromechanical system sensor has the characteristics of high precision and low latency, and can detect intracranial pressure changes as small as 0.01 mmHg, and reduce sensor drift in long-term monitoring through the self-calibration function, thereby effectively solving the drainage lag problem caused by limited sensor accuracy and sensitivity in traditional technologies. The system also uses a proportional-differential control formula combined with real-time monitored intracranial pressure data to achieve precise control of the drainage reaction speed, further improving the accuracy of drainage control.

[0024] The present invention uses an artificial intelligence processing unit and a deep neural network model, combined with the patient's physiological parameters, to establish a personalized intracranial pressure set value model, thereby achieving adaptive drainage adjustment for different patients, solving the problem that the traditional setting of intracranial pressure thresholds that relies on doctor's experience is difficult to accurately adjust according to individual differences. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the internal structure of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] like Figure 1 As shown, an embodiment of the present invention provides a real-time monitoring system for intracranial pressure and adaptive drainage control optimization, including a hardware module, a software module and a mathematical model, which monitors intracranial pressure in real time through a high-precision sensor and performs adaptive adjustments based on individual characteristics of the patient to optimize drainage control;

[0028] Its hardware modules include: high-precision pressure sensor for real-time monitoring of intracranial pressure. The high-precision pressure sensor adopts micro-electromechanical system technology, has high-precision and low-latency measurement performance, and can sense intracranial pressure changes as small as 0.01mmHg. The pressure sensor has a self-calibration function, which can automatically adjust its sensitivity during long-term monitoring to reduce sensor drift;

[0029] The drainage line is connected to the patient's anus and is used to regulate the drainage of cerebrospinal fluid. The drainage line adopts an anti-clogging design and the inner wall is coated with antibacterial material to reduce the risk of line blockage and infection during long-term use. The actuator is a micro solenoid valve or an intelligent pump with high-precision flow regulation function and can achieve continuous adjustable control of the flow rate according to the controller instructions.

[0030] Actuator, controls the opening and closing of valves or pumps in the drainage pipeline and the flow rate,

[0031] A controller receives intracranial pressure data from the sensor, calculates and executes drainage control instructions. The controller includes an artificial intelligence processing unit that can analyze the intracranial pressure data from the sensor in real time, and calculates the intracranial pressure setting value suitable for the patient through a neural network model in combination with the individual characteristics of the patient;

[0032] Among them, the software module is based on artificial intelligence algorithm. By analyzing sensor data and the patient's physiological parameters, a personalized intracranial pressure threshold model is established. The personalized intracranial pressure threshold model contains neural network and fuzzy control algorithm, which is used to dynamically adjust the drainage volume. The specific formula is as follows:

[0033]

[0034] Where: Q drain is the instantaneous drainage volume, P ICP is the real-time monitoring of intracranial pressure, P set It is a personalized intracranial pressure setting value calculated based on the patient's individual characteristics and historical data. is the rate of change of intracranial pressure, K gain is the proportional gain coefficient, used to control the drainage reaction speed, K adjust The coefficient is adaptively adjusted to dynamically adjust the drainage strategy according to the patient's physiological parameters. The fuzzy control algorithm is used to deal with nonlinear changes in intracranial pressure. The controller can provide smooth drainage adjustment according to different rates and degrees of change to avoid excessive drainage or delayed response. The artificial intelligence processing unit uses a deep neural network model to predict the trend of intracranial pressure changes based on the patient's historical monitoring data and physiological parameters, and dynamically adjust the drainage set value. The controller transmits data with external devices through the wireless communication module to achieve remote monitoring, parameter adjustment and system diagnosis. The external equipment includes a doctor's operation terminal. The doctor can view the patient's intracranial pressure monitoring data in real time through the terminal and make adjustments according to the patient's condition. The parameters of the drainage system can be adjusted remotely. The regression model adopts multivariable linear regression or nonlinear regression algorithm to dynamically adjust the set value of intracranial pressure according to the physiological differences of different patients. The system calculates the amplitude and phase of intracranial pressure fluctuations through frequency domain analysis method, and further optimizes the drainage control strategy based on statistical confidence interval to ensure the safety and effectiveness of drainage. The controller includes an alarm module. When the intracranial pressure exceeds the preset safety threshold range, the alarm is automatically triggered and the doctor is notified through the wireless communication module for processing. The alarm module can distinguish critical situations through different alarm levels according to the rapid changes in intracranial pressure or the long-term deviation from the normal range, so as to facilitate timely intervention.

[0035] This formula realizes real-time monitoring of intracranial pressure and precise drainage regulation through the proportional-differential control method combined with neural network and fuzzy control strategy, effectively solves the drainage lag problem caused by limited sensor accuracy, and dynamically adjusts the drainage setting value based on the individual characteristics of the patient.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Intracranial pressure real-time monitoring and drainage adaptive control optimization system, characterized by: include: Hardware modules, software modules and mathematical models monitor intracranial pressure in real time through high-precision sensors and make adaptive adjustments based on individual patient characteristics to optimize drainage control; Its hardware modules include: high-precision pressure sensor for real-time monitoring of intracranial pressure; Drainage line, connected to the patient's anus, used to regulate the drainage of cerebrospinal fluid; Actuator, which controls the opening and closing of valves or pumps in the drainage pipeline and the flow rate; A controller receives the intra-arterial pressure data from the sensor, calculates and executes drainage control instructions; Among them, the software module is based on artificial intelligence algorithm, and establishes a personalized intra-artificial pressure threshold model by analyzing sensor data and the patient's physiological parameters. The personalized intra-artificial pressure threshold model includes a neural network and a fuzzy control algorithm, which is used to dynamically adjust the drainage volume. The specific formula is as follows: Where: Q drain is the instantaneous drainage volume, P ICP is the real-time monitoring of intracranial pressure, P set It is a personalized intracranial pressure setting value calculated based on the patient's individual characteristics and historical data. is the rate of change of intracranial pressure, K gain is the proportional gain coefficient, used to control the drainage reaction speed, K adjust It is an adaptive adjustment coefficient that dynamically adjusts the drainage strategy according to the patient's physiological parameters.

2. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1 is characterized by: The high-precision pressure sensor adopts micro-electromechanical system technology and can sense intracranial pressure changes as small as 0.01 mmHg. The pressure sensor has a self-calibration function and can automatically adjust its sensitivity during long-term monitoring to reduce sensor drift.

3. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1 is characterized by: The drainage pipeline adopts an anti-clogging design, and the inner wall is coated with antibacterial material. The actuator is a micro solenoid valve or an intelligent pump, which has a high-precision flow regulation function and can achieve continuous adjustable control of the flow rate according to the controller instructions.

4. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1, characterized in that: The controller includes an artificial intelligence processing unit that can analyze intracranial pressure data from the sensor in real time, and calculate an intracranial pressure setting value suitable for the patient through a neural network model in combination with the patient's individual characteristics.

5. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1 is characterized by: The fuzzy control algorithm is used to process nonlinear changes in intracranial pressure. The controller can provide smooth drainage adjustment according to different rates and degrees of change to avoid excessive drainage or delayed response. The artificial intelligence processing unit uses a deep neural network model based on the patient's historical monitoring data and physiological parameters.

6. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1 is characterized by: The controller transmits data with external devices through a wireless communication module to achieve remote monitoring, parameter adjustment and system diagnosis. The external device includes a doctor operation terminal. The doctor can view the patient's intracranial pressure monitoring data in real time through the terminal and remotely adjust the parameters of the drainage system according to the patient's condition.

7. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1 is characterized by: The regression model adopts a multivariate linear regression or nonlinear regression algorithm to dynamically adjust the set value of intracranial pressure according to the physiological differences of different patients. The system calculates the amplitude and phase of intracranial pressure fluctuations through a frequency domain analysis method, and further optimizes the drainage control strategy based on the statistical confidence interval to ensure the safety and effectiveness of drainage.

8. The intracranial pressure real-time monitoring and drainage adaptive control optimization system according to claim 1, characterized in that: The controller includes an alarm module, which automatically triggers an alarm when the intracranial pressure exceeds a preset safety threshold range, and notifies a doctor through the wireless communication module for processing. The alarm module can distinguish critical situations through different alarm levels according to the rapid changes in intracranial pressure or the trend of long-term deviation from the normal range, so as to facilitate timely intervention.

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