Method and system for managing intracranial pressure after cerebral infarction thrombectomy based on fNIRS-TCD combined monitoring

Abstract

The invention relates to the technical field of medical equipment and signal processing, in particular to a cerebral infarction thrombectomy postoperative intracranial pressure management method and system based on fNIRS-TCD combined monitoring, and the method comprises the steps: firstly, synchronously collecting fNIRS cerebral blood oxygen concentration change data and TCD cerebral blood flow velocity data through a head-mounted device, and calculating a pulsation index; then, on the basis of a Bayesian data assimilation framework, constructing the data into a real observation vector, combining a state vector predicted by an intracranial physiological model and an observation vector predicted by an observation model, calculating a Kalman gain through an iterative integrated Kalman algorithm, and further correcting the state vector in real time; and obtaining state estimation containing precise intracranial pressure. And finally, the system carries out three-level early warning according to the real-time intracranial pressure data in combination with brain blood oxygen and pulsation index changes, and carries out stepped individual management from observation, drug intervention to emergency rescue according to the three-level early warning. According to the invention, the intracranial pressure management accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of medical equipment and signal processing technology, and in particular to a method and system for managing intracranial pressure after thrombectomy for cerebral infarction based on combined fNIRS-TCD monitoring. Background Technology

[0002] Despite a high rate of recanalization after mechanical thrombectomy for acute large vessel occlusive stroke, the proportion of patients with good neurological outcomes remains unsatisfactory, with intracranial pressure (ICP) management being a key challenge. Currently, there is a lack of continuous and objective ICP monitoring methods to guide individualized treatment. While invasive monitoring (such as intraventricular monitoring) is the gold standard, it carries risks such as infection, bleeding, and procedural complexity. Existing non-invasive monitoring techniques each have limitations: for example, transcranial Doppler ultrasound (TCD) is easily affected by factors such as cerebral vasospasm, resulting in insufficient accuracy; other techniques, such as optic nerve sheath diameter and photic visual evoked potentials, suffer from operational difficulties, narrow applicability, or low precision. Therefore, traditional ICP-lowering protocols relying on clinical symptoms and imaging experience struggle to achieve precise titration, easily leading to insufficient or excessive dehydration, causing reperfusion injury or cerebral hypoperfusion complications, ultimately affecting patient prognosis. Summary of the Invention

[0003] This invention provides a method and system for managing intracranial pressure after thrombectomy for cerebral infarction based on combined fNIRS-TCD monitoring. It addresses the shortcomings of existing intracranial pressure monitoring methods in terms of accuracy, safety, and continuity, thereby enabling individualized and precise management of intracranial pressure in patients after thrombectomy for cerebral infarction.

[0004] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention is to provide a method for managing intracranial pressure after thrombectomy in cerebral infarction based on combined fNIRS-TCD monitoring, comprising the following steps: S1: The fNIRS detection unit of the head-mounted detection device collects cerebral blood oxygen concentration change data corresponding to several brain regions of the user, and the TCD probe of the head-mounted detection device collects CBFV data; the pulsatility index PI is calculated from the CBFV data. S2: A true observation vector is constructed using cerebral oxygenation concentration change data, pulsatility index (PI), and cerebral blood flow velocity (CBFV) data. An initial state vector is set. Based on the state vector of the previous moment, a predicted state vector for the next moment is obtained using an intracranial physiological model. Based on the predicted state vector, a predicted observation vector is obtained using an observation model. During the iteration process of the predicted state vector, a predicted state covariance matrix is ​​obtained. Based on the predicted state covariance matrix, a Kalman gain matrix is ​​obtained by iteratively integrating the Kalman gain formula. Based on the predicted state vector, the predicted observation vector, the true observation vector, and the Kalman gain matrix, an updated state vector is obtained using a state update formula. The state vector includes intracranial pressure data. S3: Display intracranial pressure data through the updated state vector; then combine the intracranial pressure data with changes in cerebral blood oxygen concentration and pulsatility index (PI) to perform a three-level early warning of intracranial pressure, and finally manage according to the results of the three-level early warning.

[0005] Furthermore, the data on changes in cerebral blood oxygen concentration is ΔHbO / ΔHb; ΔHbO represents the change in oxyhemoglobin concentration, and ΔHb represents the change in deoxyhemoglobin concentration. The CBFV data refers to cerebral blood flow velocity data.

[0006] Furthermore, the iterative integrated Kalman gain formula is specifically expressed as follows:

[0007] In the formula, Represents the Kalman gain matrix. Represents the predicted state covariance matrix. Represents the observation model matrix, This represents the transpose of the observation model matrix. Represents the observation noise covariance matrix. This represents the adaptive regularization parameter; The predicted state covariance matrix is ​​obtained simultaneously during the iteration process of the predicted state vector; the observation noise covariance matrix is ​​determined through prior analysis during the experimental process; and the observation model matrix is ​​obtained by combining principles of fluid mechanics and physiology.

[0008] Furthermore, the state update formula is specifically expressed as follows:

[0009] In the formula, This represents the updated state vector. Represents the predicted state vector. Represents the Kalman gain matrix. Represents the true observation vector, This represents the predicted observation vector.

[0010] Furthermore, the intracranial pressure data, combined with cerebral blood oxygenation concentration changes and the pulsatility index (PI), is used to establish a three-level early warning system for intracranial pressure, including: Three levels of alerts are generated based on the updated values ​​of various status parameters; the specific process for each level of alert is as follows: When a single parameter shows a slight anomaly, a Level 1 warning is issued; specifically: When the ICP update value continues to be Or the updated value of the pulsatility index (PI) continues to be in Or, ΔHbO / ΔHb decreases relative to baseline If so, a Level 1 warning will be issued; When both parameters are moderately abnormal, a level-two warning is issued; specifically: The condition for moderate anomalies in each parameter is that the updated value of ICP remains consistently within a certain range. The updated value of the pulsatility index (PI) continues to be... The decrease in ΔHbO / ΔHb relative to the baseline is greater than A level-two warning is issued when at least two of all parameters meet the criteria for moderate anomaly. When all parameters are severely abnormal, a level three warning will be issued; specifically: The condition for severe anomalies in each parameter is that the updated value of ICP remains consistently within a certain range. The updated value of the pulsatility index (PI) continues to be... The decrease in ΔHbO / ΔHb relative to the baseline is greater than When all parameters meet the criteria for severe abnormality, a level three warning is issued. in, Indicates the first alarm threshold. This indicates the second alarm threshold. Indicates the third alarm threshold; Indicates the first pulsation threshold. This indicates the second pulsation threshold. Indicates the third pulsation threshold; This indicates the first threshold for changes in blood oxygenation. This indicates the second blood oxygenation threshold. This indicates the third blood oxygenation threshold. This indicates the fourth threshold for changes in blood oxygenation.

[0011] Furthermore, the management based on the three-level early warning results includes: At Level 1, the focus is on strengthening monitoring and investigating reversible factors; at Level 2, active drug intervention needs to be initiated; and at Level 3, the emergency rescue phase begins, requiring comprehensive measures such as enhanced dehydration, short-term hyperventilation, deep sedation, and even evaluation of decompressive craniectomy to prevent irreversible brain damage.

[0012] A second aspect of the present invention is to provide an intracranial pressure management system based on fNIRS-TCD combined monitoring after thrombectomy for cerebral infarction, comprising: Detection module: Used to collect cerebral blood oxygen concentration change data corresponding to several brain regions of the user through the fNIRS detection unit of the head-mounted detection device, and to collect CBFV data through the TCD probe of the head-mounted detection device; and to calculate the pulsatility index (PI) from the CBFV data. The integration module is used to construct a realistic observation vector using cerebral oxygenation concentration change data, pulsatility index (PI), and cerebral blood pressure fluctuation (CBFV) data. It sets an initial state vector, and based on the previous state vector, obtains a predicted state vector for the next time step using an intracranial physiological model. Based on the predicted state vector, it obtains a predicted observation vector using an observation model. During the iteration process of the predicted state vector, it simultaneously obtains the predicted state covariance matrix. Based on the predicted state covariance matrix, iteratively integrates the Kalman gain formula to obtain the Kalman gain matrix. Based on the predicted state vector, the predicted observation vector, the real observation vector, and the Kalman gain matrix, it uses a state update formula to obtain the updated state vector. The state vector includes intracranial pressure data. Output module: Used to display intracranial pressure data through the updated state vector; then, the intracranial pressure data is combined with cerebral blood oxygenation concentration change data and pulsatility index (PI) to perform three-level intracranial pressure early warning, and finally, management is carried out based on the results of the three-level early warning.

[0013] Furthermore, the detection module includes an fNIRS detection unit and a TCD probe.

[0014] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD combined monitoring.

[0015] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD joint monitoring.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: The fNIRS detection unit of the head-mounted detection device collects cerebral oxygenation concentration change data corresponding to several brain regions of the user via fNIRS, and CBFV data is collected via the TCD probe of the head-mounted detection device; the pulsatility index (PI) is calculated from the CBFV data; comprehensive non-invasive monitoring of cerebral oxygenation and hemodynamic parameters is achieved, overcoming the limitations of single monitoring technologies: fNIRS provides direct data on brain tissue oxygenation status, TCD provides large vessel hemodynamic parameters, and the calculated PI value indirectly reflects cerebrovascular resistance; this multi-parameter synchronous acquisition provides a rich and complementary observational basis for subsequent data fusion, fundamentally ensuring multi-dimensional information input for intracranial pressure assessment and laying a data foundation for accurate monitoring; a realistic observation vector is constructed using cerebral oxygenation concentration change data, PI, and CBFV data; an initial state vector is set, and based on the state vector of the previous moment, a predicted state vector for the next moment is obtained through an intracranial physiological model; based on the predicted state vector, the observation model... The system obtains predicted observation vectors; during the iteration process of the predicted state vector, the predicted state covariance matrix is ​​obtained simultaneously. Based on the predicted state covariance matrix, the Kalman gain matrix is ​​obtained by iteratively integrating the Kalman gain formula; based on the predicted state vector, predicted observation vector, actual observation vector, and Kalman gain matrix, the updated state vector is obtained through the state update formula; the state vector contains intracranial pressure data; it achieves accurate, non-invasive, and real-time calculation of intracranial pressure; using a state-space model and Kalman filtering algorithm, the system can dynamically balance the reliability of model predictions and measured data, and continuously correct the blood oxygenation information from fNIRS and blood flow information from TCD to convert them into high-fidelity intracranial pressure values, so that intracranial pressure, which cannot be directly measured, can be accurately estimated through multi-source data fusion; the updated state vector is used to display intracranial pressure data; then, the intracranial pressure data is combined with cerebral blood oxygenation concentration change data and pulsatility index (PI) to perform three-level intracranial pressure early warning, and finally, management is carried out based on the three-level early warning results; it realizes individualized intracranial pressure management based on multi-parameter linkage. By establishing a three-tiered early warning mechanism, the system can identify abnormal brain perfusion at an early stage and issue timely graded alarms. This enables clinical intervention to shift from passive response to proactive early warning, guiding medical staff to adopt a tiered management strategy based on the severity of the abnormality, from observation and drug intervention to emergency rescue. Ultimately, this achieves precise and proactive treatment, which is expected to significantly improve patient prognosis. It also addresses the shortcomings of existing intracranial pressure monitoring methods in terms of accuracy, safety, and continuity. 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This invention provides a flowchart illustrating the steps of a method for managing intracranial pressure after thrombectomy in cerebral infarction based on combined fNIRS-TCD monitoring. Figure 2 This invention provides a schematic diagram of the module flow of an intracranial pressure management system based on fNIRS-TCD combined monitoring after thrombectomy for cerebral infarction. Figure 3 This is a simulation diagram of the joint detection process. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] To address the problems existing in the background technology, a method and system for managing intracranial pressure after thrombectomy in cerebral infarction based on fNIRS-TCD combined monitoring was designed, which has important practical significance.

[0022] like Figure 1 As shown, the first aspect of the present invention is to provide a method for managing intracranial pressure after thrombectomy for cerebral infarction based on combined fNIRS-TCD monitoring, comprising the following steps: Step S1: Collect cerebral oxygen concentration change data corresponding to several brain regions of the user through the fNIRS detection unit of the head-mounted detection device, and collect CBFV data through the TCD probe of the head-mounted detection device; calculate the pulsatility index (PI) through the CBFV data.

[0023] Specifically, a head-mounted detection device was used to collect data on changes in brain blood oxygen concentration (physiological parameters such as ΔHbO / ΔHb) at 31 detection points across multiple channels in several brain regions. The data was collected using a functional near-infrared spectroscopy (fNIRS) detection unit. The brain regions included the frontal cortex, motor cortex, parietal cortex, and occipital cortex. There were eight channels in this area.

[0024] The inner and outer edges of the ring-shaped support of the head-mounted detection device are adjustable ring-shaped polyurethane supports. Using the temporal region of the ring-shaped support as a reference point, a sliding adjustable TCD (Transcranial Doppler) probe, approximately 1 cm in diameter, is fixed on each side to acquire CBFV data. The pulsatility index (PI) is calculated from the raw spectral waveform signal (CBFV data) acquired by the TCD probes. The calculation process of the PI is a well-known technique and will not be described in detail here.

[0025] Among them, CBFV represents cerebral blood flow velocity, which is a key parameter for assessing cerebral vascular patency and spasm; ΔHbO represents the change in oxyhemoglobin concentration; and ΔHb represents the change in deoxyhemoglobin concentration.

[0026] Among them, a realistic observation vector was constructed using data on changes in cerebral blood oxygen concentration, pulsatility index (PI), and CBFV.

[0027] At this point, the actual observation vectors are obtained.

[0028] Step S2: Construct a true observation vector using cerebral oxygenation concentration change data, pulsatility index (PI), and CBFV data; set an initial state vector; based on the previous state vector, obtain the predicted state vector for the next time step using an intracranial physiological model; based on the predicted state vector, obtain the predicted observation vector using an observation model; simultaneously obtain the predicted state covariance matrix during the iteration process of the predicted state vector; based on the predicted state covariance matrix, obtain the Kalman gain matrix by iteratively integrating the Kalman gain formula; based on the predicted state vector, the predicted observation vector, the true observation vector, and the Kalman gain matrix, obtain the updated state vector using a state update formula.

[0029] Sub-step S21: State prediction.

[0030] This step is based on a physical model, predicting the current state from the state of the previous time step.

[0031] This step inputs the state vector from the previous time step; this state vector contains the system's estimates of all key variables, specifically: estimated intracranial pressure, estimated cerebral blood flow, estimated vascular volume, and estimated cerebral oxygen concentration. In other words, the characteristic variables contained in the state vector are intracranial pressure, cerebral blood flow, vascular volume, and cerebral oxygen concentration.

[0032] An initial state vector is set, and the predicted state vector for the next moment is obtained based on the state vector of the previous moment through an intracranial physiological model; where the intracranial physiological model is a state transition model.

[0033] Among them, the intracranial physiological model includes hemodynamic equations (describing blood flow in blood vessels), vascular wall mechanics equations (describing the elasticity of blood vessels), intracranial pressure-volume relationship, and brain autoregulation model (describing the physiological control of cerebral blood flow). The hemodynamic equations include the mass conservation equation and the momentum conservation equation; the specific formula for the mass conservation equation is as follows:

[0034] In the formula, This represents the cross-sectional area of ​​the blood vessel. Indicates the average blood flow rate. Indicates time, This indicates displacement along the direction of the blood vessel. The symbol represents the partial derivative.

[0035] The specific formula for the momentum conservation equation is as follows:

[0036] In the formula, This represents the velocity profile correction factor (related to the shape of the blood flow velocity distribution within the blood vessel). Indicates blood density, Indicates the blood pressure within the blood vessels. Indicates blood viscosity. Indicates the radius of the blood vessel. This represents the velocity gradient at the vessel wall, which is related to the shear stress experienced by the blood flow.

[0037] The specific formula for the constitutive equation of the blood vessel wall in the blood vessel wall mechanics equation is as follows:

[0038] In the formula, This indicates the reference pressure (usually intracranial pressure, ICP). Indicates the thickness of the blood vessel wall. This represents the Young's modulus of the blood vessel wall (characterizing elasticity). The Poisson's ratio represents the material of the blood vessel wall. Indicates reference pressure The cross-sectional area of ​​the blood vessel below.

[0039] This equation relates the pressure (p) inside the blood vessel to the degree of vascular dilation (A). It describes how the size of a blood vessel, as an elastic material, changes with variations in internal pressure.

[0040] The specific formula for the overall mechanical balance of the intracranial cavity in the intracranial pressure-volume relationship is as follows:

[0041] In the formula, This represents the pressure difference across the blood vessel wall. Indicates the radius after the blood vessel deforms. This represents intracranial pressure, which is one of the core objectives that the model needs to correlate and solve. Indicates the thickness of the relevant organization. Indicates elastic tension. Indicates passive tension. This represents muscle tension. The equation describes the macroscopic balance of various forces within the cranial cavity (from blood vessels, brain tissue, cerebrospinal fluid, etc.), and is crucial in connecting local vascular dynamics with overall intracranial pressure.

[0042] The brain autoregulation model includes a dynamic model of brain autoregulation and a differential equation of cranial cavity volume-pressure. The specific formula for the dynamic model of brain autoregulation is as follows:

[0043] In the formula, This represents the time constant of the automatic adjustment process. This represents the state variable that automatically adjusts. This indicates the automatically adjusting gain. Indicates current cerebral blood flow. This represents normal (baseline) cerebral blood flow. The differential equation simulates the dynamic process by which the brain regulates itself by dilating or constricting blood vessels in response to changes in blood pressure or intracranial pressure in order to maintain stable blood flow.

[0044] The specific formula for the intracranial cavity volume-pressure differential equation is as follows:

[0045] In the formula, It indicates the compliance of the cranial cavity (characterizing the cranial cavity's ability to accommodate additional volume). Indicates intracranial pressure. This represents the rate of change in volume of the k-th type of intracranial contents (such as arterial blood, venous blood, and cerebrospinal fluid). Indicates the possible inflow / outflow flows. This indicates normal outflow.

[0046] Sub-step S22: Observation and prediction.

[0047] This step maps the predicted state to the sensor dimensions that we can observe.

[0048] Specifically, based on the predicted state vector, the predicted observation vector is obtained through the observation model; where the observation model is a function or matrix that maps the system's state vector to the observation vector.

[0049] The observation model is derived theoretically by combining principles of fluid mechanics and physiology (which are well-known), and the model parameters are calibrated and verified using animal experiments or clinical invasive monitoring data.

[0050] Sub-step S23: Assimilation and Update.

[0051] This step compares and fuses the sensor’s actual readings with the predicted observation vector from the previous step to obtain the optimal estimate.

[0052] Specifically, the predicted state covariance matrix is ​​obtained simultaneously during the iterative process of the predicted state vector. Based on the predicted state covariance matrix, the Kalman gain matrix is ​​obtained by iteratively integrating the Kalman gain formula. The iteratively integrating Kalman gain formula is specifically expressed as follows:

[0053] In the formula, Represents the Kalman gain matrix. Represents the predicted state covariance matrix. Represents the observation model matrix, This represents the transpose of the observation model matrix. Represents the observation noise covariance matrix. This represents the adaptive regularization parameter.

[0054] The predicted state covariance matrix is ​​generated during the state prediction process. The observation noise covariance matrix is ​​determined through prior analysis during the experiment.

[0055] Sub-step S24: State correction and output.

[0056] Specifically, based on the predicted state vector, the predicted observation vector, the actual observation vector, and the Kalman gain matrix, the updated state vector is obtained using the state update formula; whereby the state update formula is specifically expressed as:

[0057] In the formula, This represents the updated state vector. Represents the predicted state vector. Represents the Kalman gain matrix. Represents the true observation vector, This represents the predicted observation vector.

[0058] Thus, the updated state vector is obtained using the method described above.

[0059] Step S3: Display intracranial pressure data using the updated state vector; then combine the intracranial pressure data with changes in cerebral blood oxygen concentration and pulsatility index (PI) to perform a three-level early warning for intracranial pressure, and finally manage the situation based on the results of the three-level early warning.

[0060] Specifically, the updated values ​​of each state parameter are determined through the updated state vector; this includes an ICP (Intracranial Pressure) trend curve, where the horizontal axis of the ICP trend curve represents time and the vertical axis represents the ICP value. A simulation diagram of the joint detection process is shown below. Figure 3 As shown.

[0061] The first alarm threshold for preset ICP values Second alarm threshold Third alarm threshold In this embodiment, a first alarm threshold with a preset ICP value is provided. Second alarm threshold Third alarm threshold In this embodiment, no specific limit is set for all alarm thresholds of the preset ICP value; the implementer can determine the threshold according to the specific circumstances.

[0062] The first pulsatility threshold of the preset pulsatility index (PI) Second pulsation threshold Third pulsation threshold In this embodiment, a first pulsation threshold with a preset pulsation index PI is provided. Second pulsation threshold Third pulsation threshold In this embodiment, no specific limitation is made on all pulsation thresholds of the preset pulsation index PI; the implementer may determine this according to the specific circumstances.

[0063] Three preset blood oxygen change thresholds are defined as follows: First blood oxygen change threshold... Second blood oxygenation threshold Third blood oxygenation threshold Fourth blood oxygenation threshold In this embodiment, the first blood oxygen change threshold Second blood oxygenation threshold Third blood oxygenation threshold Fourth blood oxygenation threshold In this embodiment, the three preset blood oxygen change thresholds are not specifically limited, and the implementer can determine them according to the specific circumstances.

[0064] Three levels of alerts are generated based on the updated values ​​of various status parameters; the specific process for each level of alert is as follows: When a single parameter shows a slight anomaly, a Level 1 warning is issued; specifically: When the ICP update value continues to be Or the updated value of the pulsatility index (PI) continues to be in Or, ΔHbO / ΔHb decreases relative to baseline If so, a Level 1 warning will be issued; When both parameters are moderately abnormal, a level-two warning is issued; specifically: The condition for moderate anomalies in each parameter is that the updated value of ICP remains consistently within a certain range. The updated value of the pulsatility index (PI) continues to be... The decrease in ΔHbO / ΔHb relative to the baseline is greater than A level-two warning is issued when at least two of all parameters meet the criteria for moderate anomaly. When all parameters are severely abnormal, a level three warning will be issued; specifically: The condition for severe anomalies in each parameter is that the updated value of ICP remains consistently within a certain range. The updated value of the pulsatility index (PI) continues to be... The decrease in ΔHbO / ΔHb relative to the baseline is greater than When all parameters meet the criteria for severe anomalies, a level three warning is issued.

[0065] The percentage decrease in ΔHbO / ΔHb relative to the baseline is obtained by multiplying the difference between each data point and the baseline by the baseline ratio. In this embodiment, a duration of more than 1 minute is considered continuous; however, the duration is not specifically limited in this embodiment and can be determined by the implementer according to specific circumstances.

[0066] The intracranial pressure management process based on a three-level warning system is as follows: Level 1 warning (mild abnormality of a single parameter) focuses on strengthening monitoring and investigating reversible factors, such as adjusting body position and providing analgesia and sedation; Level 2 warning (moderate abnormality of two parameters) requires active drug intervention, such as infusion of mannitol and other dehydrating agents to maintain cerebral perfusion pressure; Level 3 warning (severe abnormality of parameters or linked changes) enters the emergency rescue stage, requiring comprehensive measures such as intensive dehydration, short-term hyperventilation, deep sedation, and even evaluation of decompressive craniectomy to prevent irreversible brain damage.

[0067] This concludes the embodiment.

[0068] like Figure 2 As shown, a second aspect of the present invention is to provide an intracranial pressure management system based on fNIRS-TCD combined monitoring after thrombectomy for cerebral infarction, comprising: Detection module 101: Used to collect cerebral blood oxygen concentration change data corresponding to several brain regions of the user via the fNIRS detection unit of the head-mounted detection device, collect CBFV data via the TCD probe of the head-mounted detection device, and calculate the pulsatility index PI from the CBFV data; Integration module 102: This module constructs a realistic observation vector using cerebral oxygenation concentration change data, pulsatility index (PI), and cerebral blood pressure fluctuation (CBFV) data. It sets an initial state vector, and based on the previous state vector, obtains a predicted state vector for the next time step using an intracranial physiological model. Based on the predicted state vector, it obtains a predicted observation vector using an observation model. During the iteration process of the predicted state vector, it simultaneously obtains the predicted state covariance matrix. Based on the predicted state covariance matrix, iteratively integrates the Kalman gain formula to obtain the Kalman gain matrix. Based on the predicted state vector, the predicted observation vector, the real observation vector, and the Kalman gain matrix, it uses a state update formula to obtain the updated state vector. The state vector includes intracranial pressure data. Output module 103: used to display intracranial pressure data through the updated state vector; then, the intracranial pressure data is combined with cerebral blood oxygen concentration change data and pulsatility index (PI) to perform a three-level early warning of intracranial pressure, and finally, management is carried out based on the results of the three-level early warning.

[0069] The detection module includes an fNIRS detection unit and a TCD probe.

[0070] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD combined monitoring.

[0071] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD joint monitoring.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD combined monitoring, characterized in that, The method comprises the following steps: S1: collecting brain blood oxygen concentration change data corresponding to fNIRS of several brain regions of a user through an fNIRS detection unit of a head-mounted detection device, collecting CBFV data through a TCD probe of the head-mounted detection device, and calculating a pulsatility index PI from the CBFV data; S2: constructing a real observation vector from the brain blood oxygen concentration change data, the pulsatility index PI, and the CBFV data; setting an initial state vector, obtaining a predicted state vector at the next moment from the state vector at the previous moment through an intracranial physiological model, obtaining a predicted observation vector from the predicted state vector through an observation model, obtaining a predicted state covariance matrix in the iteration process of the predicted state vector, obtaining a Kalman gain matrix from the predicted state covariance matrix through an iterative integrated Kalman gain formula, and obtaining an updated state vector from the predicted state vector, the predicted observation vector, the real observation vector, and the Kalman gain matrix through a state update formula; the state vector comprises intracranial pressure data; S3: displaying the intracranial pressure data through the updated state vector, performing three-level early warning of intracranial pressure by combining the intracranial pressure data with the brain blood oxygen concentration change data and the pulsatility index PI, and managing according to the three-level early warning results.

2. The method for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD combined monitoring according to claim 1, characterized in that, The brain blood oxygen concentration change data is ΔHbO / ΔHb, wherein ΔHbO represents the change amount of oxygenated hemoglobin concentration, and ΔHb represents the change value of deoxygenated hemoglobin concentration. The CBFV data is brain blood flow velocity data.

3. The method for managing intracranial pressure after cerebral infarction thrombectomy according to claim 1, characterized in that, The iterative integrated Kalman gain formula is specifically represented as follows: wherein denotes the Kalman gain matrix, denotes the predicted state covariance matrix, denotes the observation model matrix, denotes the transpose of the observation model matrix, denotes the observation noise covariance matrix, denotes the adaptive regularization parameter; wherein the predicted state covariance matrix is obtained in the iteration process of the predicted state vector; wherein the observation noise covariance matrix is determined through prior analysis in an experimental process; and wherein the observation model matrix is obtained by combining fluid mechanics and physiological principles.

4. The method for managing intracranial pressure after cerebral infarction thrombectomy according to claim 1, characterized in that, The state update formula is specifically represented as follows: wherein denotes the updated state vector, denotes the predicted state vector, denotes the Kalman gain matrix, denotes the real observation vector, denotes the predicted observation vector.

5. The method for managing intracranial pressure after cerebral infarction thrombectomy according to claim 2, characterized in that, The three-level early warning of intracranial pressure by combining the intracranial pressure data with the brain blood oxygen concentration change data and the pulsatility index PI comprises the following steps: three-level early warning is performed through the updated values of various state parameters; the specific process of the three-level early warning is as follows: when a single parameter is slightly abnormal, first-level early warning is performed; specifically, When the updated value of ICP persists in or the updated value of pulse index PI persists in or ΔHbO / ΔHb decreases relative to baseline , a primary warning is given; when two parameters are moderately abnormal, second-level early warning is performed; specifically, The condition for moderate abnormality in each parameter is that the updated value of ICP is continuously greater than , the updated value of PI is continuously greater than , and ΔHbO / ΔHb is decreased by more than relative to the baseline; when at least two of all the parameters meet the condition for moderate abnormality, a secondary warning is given; when all parameters are severely abnormal, third-level early warning is performed; specifically, The condition for each parameter to be severely abnormal is that the updated value of ICP is continuously greater than , the updated value of PI is continuously greater than , and the decrease of ΔHbO / ΔHb relative to the baseline is greater than ; when all parameters meet the condition for severe abnormality, a level 3 early warning is performed. wherein represents a first alarm threshold, represents a second alarm threshold, represents a third alarm threshold; represents a first beat threshold, represents a second beat threshold, represents a third beat threshold; represents a first blood oxygen change threshold, represents a second blood oxygen change threshold, represents a third blood oxygen change threshold, represents a fourth blood oxygen change threshold.

6. The method for managing intracranial pressure after cerebral infarction thrombectomy according to claim 1, characterized in that, the management according to the three-level early warning results comprises the following steps: when first-level early warning is performed, the main task is to strengthen monitoring and investigate reversible factors; when second-level early warning is performed, active drug intervention is started; when third-level early warning is performed, an emergency rescue stage is entered, and comprehensive measures such as intensified dehydration, short-term excessive ventilation, deep sedation, and even evaluation of decompressive craniectomy surgery are taken to prevent irreversible brain damage.

7. The system for managing intracranial pressure after thrombectomy for cerebral infarction based on fNIRS-TCD combined monitoring, characterized in that, The method comprises the following steps: a detection module: collecting brain blood oxygen concentration change data corresponding to fNIRS of several brain regions of a user through an fNIRS detection unit of a head-mounted detection device, collecting CBFV data through a TCD probe of the head-mounted detection device, and calculating a pulsatility index PI from the CBFV data; The integration module is used to construct a real observation vector through the brain blood oxygen concentration change data, the pulsatility index PI and the CBFV data; set an initial time state vector, obtain a next time predicted state vector through an intracranial physiological model according to a last time state vector; obtain a predicted observation vector through an observation model according to the predicted state vector; obtain a predicted state covariance matrix in the iteration process of the predicted state vector, obtain a Kalman gain matrix through an iterative integrated Kalman gain formula according to the predicted state covariance matrix; obtain an updated state vector through a state update formula according to the predicted state vector, the predicted observation vector, the real observation vector and the Kalman gain matrix; the state vector contains the intracranial pressure data; The output module is used to display the intracranial pressure data through the updated state vector; then the intracranial pressure data is combined with the brain blood oxygen concentration change data and the pulsatility index PI to perform a three-level early warning of the intracranial pressure; finally, the three-level early warning results are used for management.

8. The fNIRS-TCD combined monitoring based management system for intracranial pressure after cerebral embolism thrombectomy according to claim 7, characterized in that, The detection module includes an fNIRS detection unit and a TCD probe.

9. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the intracranial pressure management method after the thrombectomy for cerebral infarction based on the fNIRS-TCD combined monitoring according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the intracranial pressure management method after the thrombectomy for cerebral infarction based on the fNIRS-TCD combined monitoring according to any one of claims 1-6.

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