A method and system for simulating intracranial pressure in an intracranial hematoma suction environment

Abstract

The present invention relates to the field of medical technology, and discloses a method and system for simulating intracranial pressure in an intracranial hematoma suction environment, the method comprising: simulating intracranial pressure changes based on each time point according to intracranial pressure physiological parameters, obtaining corresponding time windows and intracranial pressure change trend signals; adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, generating respiratory pulse signals; simulating instantaneous intracranial pressure changes caused by heartbeats based on each heartbeat cycle, obtaining intracranial pressure change pulse signals for each heartbeat cycle, integrating intracranial pressure change pulse signals, obtaining intracranial pressure pulse sequences; simulating the extraction / injection process at each time point, generating corresponding intracranial pressure change signals in the extraction / injection process; integrating intracranial pressure change trend signals, respiratory pulse signals, intracranial pressure pulse sequences, and intracranial pressure change signals, obtaining total intracranial pressure signals. The present invention can more accurately simulate and analyze the influence of intracranial hematoma suction process on intracranial pressure.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method and system for simulating intracranial pressure in an intracranial hematoma suction environment. Background Art

[0002] Intracranial pressure (ICP) is an important physiological indicator that reflects the pressure of various intracranial structures (such as brain tissue, cerebrospinal fluid, and blood) on the skull. Changes in intracranial pressure are associated with a variety of disease states. For example, intracranial hypertension (ICP exceeding 15 mmHg) can be caused by factors such as head trauma, brain tumors, hemorrhage, and edema, which may lead to serious complications or even life-threatening. Therefore, monitoring intracranial pressure has extremely important clinical value in disease assessment and intracranial pressure reduction treatment.

[0003] Globally, cerebrovascular disease is one of the leading causes of death, especially hemorrhagic stroke, which has a higher mortality and disability rate than other types of stroke. Hemorrhagic stroke often involves the formation of hematoma, and the space-occupying effect of hematoma and its toxic effects on surrounding brain tissue are key factors leading to further brain damage. Therefore, timely and effective removal of hematoma, while minimizing surgical damage and preventing rebleeding, is the key to the treatment of hemorrhagic stroke.

[0004] In the field of neurosurgery, the treatment and management of intracranial hematoma is an important topic, and precise control of intracranial pressure is crucial for hematoma treatment. At present, some existing intracranial hematoma removal systems use multiple modules to achieve real-time monitoring of key information of patients during the treatment of intracranial hematoma, making the treatment operation more controllable and accurate. For example, the intelligent intracranial hematoma removal and drainage system disclosed in publication number CN109663153A achieves this goal through an information input module, an information detection module, an intelligent module, and a hematoma removal and drainage module. However, there are still challenges in obtaining high-quality clinical data, so there is an urgent need for a simulation method that can accurately simulate the intracranial hematoma aspiration process and its impact on intracranial pressure. Summary of the invention

[0005] The embodiment of the present invention provides a method and system for simulating intracranial pressure in an intracranial hematoma suction environment to solve the above-mentioned technical problems in the prior art.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for simulating intracranial pressure in an intracranial hematoma suction environment is provided.

[0008] In one embodiment, the intracranial pressure simulation method in the intracranial hematoma suction environment includes:

[0009] According to the physiological parameters of intracranial pressure, the intracranial pressure change is simulated at each time point to obtain the corresponding time window and intracranial pressure change trend signal;

[0010] Using the time window as a respiratory cycle window, and adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, and generating a respiratory pulse signal of the same length as the intracranial pressure change trend signal;

[0011] Based on each heartbeat cycle, the heartbeat interval, the pulse amplitude and shape of the intracranial pressure change within the heartbeat cycle are adjusted to simulate the instantaneous pressure change of the intracranial pressure caused by the heartbeat, and the intracranial pressure change pulse signal of each heartbeat cycle is obtained, and the intracranial pressure change pulse signals of each heartbeat cycle are integrated to obtain the intracranial pressure pulse sequence;

[0012] According to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, the extraction / injection process at each time point is simulated to generate the intracranial pressure change signal during the corresponding extraction / injection process;

[0013] The intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal are integrated to obtain a total intracranial pressure signal.

[0014] In one embodiment, according to the intracranial pressure physiological parameters, simulating the intracranial pressure change based on each time point, and obtaining the corresponding time window and intracranial pressure change trend signal includes:

[0015] Constructing an intracranial pressure physiological model and collecting intracranial pressure physiological parameters of patients, wherein the intracranial pressure physiological parameters include: cerebral blood volume, vascular resistance, arterial blood pressure, intracranial blood flow coefficient and variable, cerebrospinal fluid dynamic coefficient and variable;

[0016] Generate an intracranial pressure physiological vector according to the intracranial pressure physiological parameters of the patient and based on the intracranial pressure physiological model; wherein the intracranial pressure physiological vector includes: cerebrospinal fluid volume, intracranial pressure, and arterial exchange area;

[0017] According to the intracranial pressure physiological vector, the Sigmoid function is used to simulate the intracranial pressure change at each time point to obtain the corresponding time window and the intracranial pressure change trend signal.

[0018] In one embodiment, the intracranial pressure physiological model is the Ursino-Lodi physiological model.

[0019] In one embodiment, adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes and generating a respiratory pulse signal of equal length to the intracranial pressure change trend signal includes: adjusting the respiratory frequency and shape based on the respiratory cycle window, using the respiration_signal function to simulate intracranial pressure changes, and generating a respiratory pulse signal of equal length to the intracranial pressure change trend signal.

[0020] In one embodiment, based on each heartbeat cycle, the heartbeat interval, pulse amplitude and shape are adjusted to simulate the instantaneous pressure change of intracranial pressure caused by the heartbeat to obtain the intracranial pressure change pulse signal of each heartbeat cycle, including: based on each heartbeat cycle, the heartbeat interval, pulse amplitude and shape are adjusted, and the instantaneous pressure change of intracranial pressure caused by the heartbeat is simulated by the generateICPpulse function to obtain the intracranial pressure change pulse signal of each heartbeat cycle.

[0021] In one embodiment, according to a predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, the extraction / injection process at each time point is simulated, and a corresponding intracranial pressure change signal during the extraction / injection process is generated, including: according to a predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, the simulate_treatment function is used to simulate the extraction / injection process at each time point, and a corresponding intracranial pressure change signal during the extraction / injection process is generated.

[0022] In one embodiment, the method for simulating intracranial pressure in an intracranial hematoma suction environment further includes: performing noise distortion and filtering processing on the total intracranial pressure signal to obtain a final intracranial pressure signal.

[0023] According to a second aspect of an embodiment of the present invention, a system for simulating intracranial pressure in an intracranial hematoma suction environment is provided.

[0024] In one embodiment, the intracranial hematoma suction environment intracranial pressure simulation system comprises:

[0025] Intracranial pressure-physiological simulation module, used to simulate the change of intracranial pressure at each time point according to the intracranial pressure physiological parameters, and obtain the corresponding time window and intracranial pressure change trend signal;

[0026] An intracranial pressure-respiration simulation module, used for taking the time window as a respiratory cycle window, adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, and generating a respiratory pulse signal of the same length as the intracranial pressure change trend signal;

[0027] The intracranial pressure-heartbeat simulation module is used to adjust the heartbeat interval, the pulse amplitude and shape of the intracranial pressure change within the heartbeat cycle to simulate the instantaneous pressure change of the intracranial pressure caused by the heartbeat, obtain the intracranial pressure change pulse signal of each heartbeat cycle, and integrate the intracranial pressure change pulse signals of each heartbeat cycle to obtain the intracranial pressure pulse sequence;

[0028] The intracranial pressure-hematoma simulation module is used to simulate the extraction / injection process at each time point according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and generate the intracranial pressure change signal during the corresponding extraction / injection process;

[0029] The intracranial pressure signal integration module is used to integrate the intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal to obtain a total intracranial pressure signal.

[0030] In one embodiment, the intracranial pressure-physiological simulation module constructs an intracranial pressure physiological model and collects the patient's intracranial pressure physiological parameters when simulating the intracranial pressure change based on each time point according to the intracranial pressure physiological parameters and obtaining the corresponding time window and intracranial pressure change trend signal; generates an intracranial pressure physiological vector based on the intracranial pressure physiological parameters of the patient and based on the intracranial pressure physiological model; and simulates the intracranial pressure change based on each time point using a Sigmoid function according to the intracranial pressure physiological vector to obtain the corresponding time window and intracranial pressure change trend signal;

[0031] Among them, the intracranial pressure physiological parameters include: cerebral blood volume, vascular resistance, arterial blood pressure, intracranial blood flow coefficient and variable, cerebrospinal fluid dynamic coefficient and variable; the intracranial pressure physiological vector includes: cerebrospinal fluid volume, intracranial pressure, and arterial exchange area.

[0032] In one embodiment, the intracranial pressure physiological model is the Ursino-Lodi physiological model.

[0033] In one embodiment, the intracranial pressure-respiration simulation module adjusts the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, and generates a respiratory pulse signal of equal length to the intracranial pressure change trend signal. The intracranial pressure-respiration simulation module adjusts the respiratory frequency and shape based on the respiratory cycle window, uses the respiration_signal function to simulate intracranial pressure changes, and generates a respiratory pulse signal of equal length to the intracranial pressure change trend signal.

[0034] In one embodiment, the intracranial pressure-heartbeat simulation module adjusts the heartbeat interval, the pulse amplitude and shape of the intracranial pressure change within the heartbeat cycle to simulate the instantaneous pressure change of intracranial pressure caused by the heartbeat, and obtains the intracranial pressure change pulse signal of each heartbeat cycle. Based on each heartbeat cycle, the intracranial pressure-heartbeat simulation module adjusts the heartbeat interval, the pulse amplitude and shape, and uses the generateICPpulse function to simulate the instantaneous pressure change of intracranial pressure caused by the heartbeat to obtain the intracranial pressure change pulse signal of each heartbeat cycle.

[0035] In one embodiment, the intracranial pressure-hematoma simulation module simulates the extraction / injection process at each time point according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and generates the intracranial pressure change signal during the corresponding extraction / injection process. According to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, the simulate_treatment function is used to simulate the extraction / injection process at each time point, and the intracranial pressure change signal during the corresponding extraction / injection process is generated.

[0036] In one embodiment, the intracranial pressure simulation system in the intracranial hematoma suction environment further includes: a signal processing module for performing noise distortion and filtering processing on the total intracranial pressure signal to obtain a final intracranial pressure signal.

[0037] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0038] The present invention can generate long-term intracranial pressure trend signals and change signals of the effects of breathing, heartbeat and hematoma suction changes on intracranial pressure, and then comprehensively form an intracranial pressure signal that is close to the actual situation, thereby enhancing the ability to simulate intracranial pressure changes, and can more accurately simulate and analyze the effects of intracranial hematoma suction process on intracranial pressure, providing a reference for formulating personalized hematoma removal treatment plans, supporting in-depth research on intracranial hematoma suction and its effects on intracranial pressure, and can test and optimize different treatment modes and parameters, thereby reducing the difficulty and risks of clinical trials.

[0039] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0041] Figure 1 It is a flow chart of a method for simulating intracranial pressure in an intracranial hematoma suction environment according to an exemplary embodiment;

[0042] Figure 2 It is a structural block diagram of an intracranial pressure simulation system for intracranial hematoma suction environment according to an exemplary embodiment;

[0043] Figure 3 is a schematic diagram of intracranial pressure generated by simulation of the Ursino-Lodi physiological model according to an exemplary embodiment;

[0044] Figure 4 is a schematic diagram showing the effect of heartbeat on intracranial pressure when the sampling frequency is set to 125 Hz and the simulation duration is set to 6000 s according to an exemplary embodiment;

[0045] Figure 5 is a schematic diagram showing the effect of heartbeat on intracranial pressure in a short period of time when the sampling frequency is set to 20 Hz and the simulation duration is set to 1000 s according to an exemplary embodiment;

[0046] Figure 6 is a schematic diagram showing the effect of breathing on intracranial pressure when the sampling frequency is set to 125 Hz and the simulation duration is set to 6000 s according to an exemplary embodiment;

[0047] Figure 7 is a schematic diagram showing the effect of breathing on intracranial pressure in a short period of time when the sampling frequency is set to 20 Hz and the simulation time is set to 1000 s according to an exemplary embodiment;

[0048] Figure 8 is a schematic diagram of an integrated intracranial pressure signal according to an exemplary embodiment;

[0049] Fig. 9 is a schematic diagram of an electrical circuit of the Ursino-Lodi physiological model according to an exemplary embodiment;

[0050] Fig.10 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment.

[0051] Reference numerals:

[0052] is the arterial blood pressure, which indicates the pressure where the heart pumps; is intracranial compliance, the responsiveness of the cranial cavity to changes in pressure; It is the intracranial pressure; Indicates the pressure conversion between the chest cavity and the skull during heartbeat; is arterial resistance, which indicates the vascular resistance of the arteries from the heart to the brain; is arterial compliance, which refers to the elasticity of the arterial wall, that is, its ability to expand and contract in the face of pressure changes; is the proximal venous resistance, which refers to the resistance of the venous vessels close to the brain; Distal venous resistance refers to the resistance of the veins away from the brain. Indicates the pressure at the venous end; Indicates changes in chest pressure during breathing; Indicates the pressure conversion between the chest cavity and the skull during breathing; is the filtration resistance, which indicates the resistance of blood passing through the capillaries into the cerebrospinal fluid space; is the reabsorption resistance, which represents the resistance before cerebrospinal fluid is reabsorbed into the venous system; Indicates the effect of hematoma aspiration on intracranial pressure. DETAILED DESCRIPTION

[0053] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0054] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0055] As used herein, unless otherwise specified, the term "plurality" means two or more than two.

[0056] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0057] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0058] It should be understood that, although the various steps in the flow chart are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0059] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0060] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0061] Figure 1 An embodiment of a method for simulating intracranial pressure in an intracranial hematoma suction environment of the present invention is shown.

[0062] In this optional embodiment, the intracranial pressure simulation method in the intracranial hematoma suction environment includes:

[0063] Step S101, simulating intracranial pressure changes at each time point according to intracranial pressure physiological parameters, and obtaining corresponding time windows and intracranial pressure change trend signals;

[0064] Step S103, taking the time window as a respiratory cycle window, and adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, and generating a respiratory pulse signal of the same length as the intracranial pressure change trend signal;

[0065] Step S105, based on each heartbeat cycle, adjusting the heartbeat interval, pulse amplitude and shape to simulate the instantaneous intracranial pressure change caused by the heartbeat, obtaining an intracranial pressure change pulse signal of each heartbeat cycle, and integrating the intracranial pressure change pulse signals of each heartbeat cycle to obtain an intracranial pressure pulse sequence;

[0066] Step S107, simulating the extraction / injection process at each time point according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and generating an intracranial pressure change signal during the corresponding extraction / injection process;

[0067] Step S109, integrating the intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal to obtain a total intracranial pressure signal.

[0068] Figure 2 An embodiment of an intracranial pressure simulation system for intracranial hematoma suction environment of the present invention is shown.

[0069] In this optional embodiment, the intracranial hematoma suction environment intracranial pressure simulation system includes:

[0070] Intracranial pressure-physiological simulation module 201, used to simulate the change of intracranial pressure at each time point according to the intracranial pressure physiological parameters, and obtain the corresponding time window and intracranial pressure change trend signal;

[0071] An intracranial pressure-respiration simulation module 203, used for taking the time window as a respiratory cycle window, adjusting the respiratory frequency and shape based on the respiratory cycle window to simulate intracranial pressure changes, and generating a respiratory pulse signal of the same length as the intracranial pressure change trend signal;

[0072] The intracranial pressure-heartbeat simulation module 205 is used to adjust the heartbeat interval, pulse amplitude and shape based on each heartbeat cycle to simulate the instantaneous pressure change of intracranial pressure caused by the heartbeat, obtain the intracranial pressure change pulse signal of each heartbeat cycle, and integrate the intracranial pressure change pulse signals of each heartbeat cycle to obtain the intracranial pressure pulse sequence;

[0073] The intracranial pressure-hematoma simulation module 207 is used to simulate the extraction / injection process at each time point according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and generate an intracranial pressure change signal during the corresponding extraction / injection process;

[0074] The intracranial pressure signal integration module 209 is used to integrate the intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal to obtain a total intracranial pressure signal.

[0075] In the above embodiment, when the intracranial pressure changes are simulated based on each time point according to the intracranial pressure physiological parameters to obtain the corresponding time window and the intracranial pressure change trend signal, an intracranial pressure physiological model can be constructed, and the patient's intracranial pressure physiological parameters can be collected, wherein the intracranial pressure physiological parameters include: cerebral blood volume, vascular resistance, arterial blood pressure, intracranial blood flow coefficient and variables, cerebrospinal fluid dynamic coefficient and variables; and according to the patient's intracranial pressure physiological parameters, based on the intracranial pressure physiological model, an intracranial pressure physiological vector is generated; wherein the intracranial pressure physiological vector includes: cerebrospinal fluid volume, intracranial pressure, arterial exchange area; and then according to the intracranial pressure physiological vector, the intracranial pressure changes are simulated based on each time point using the Sigmoid function to obtain the corresponding time window and the intracranial pressure change trend signal. Specifically, the intracranial pressure physiological model is the Ursino-Lodi physiological model.

[0076] In the above embodiment, when the respiratory frequency and shape are adjusted based on the respiratory cycle window to simulate the intracranial pressure change and a respiratory pulse signal of equal length to the intracranial pressure change trend signal is generated, the respiratory frequency and shape are adjusted based on the respiratory cycle window, and the respiration_signal function is used to simulate the intracranial pressure change and a respiratory pulse signal of equal length to the intracranial pressure change trend signal is generated.

[0077] Specifically, the respiration_signal function first sets the respiratory rate and respiratory cycle counter, initializes the time pointer and the array for storing the respiratory signal, creates a respiratory rate vector with the same length as the intracranial pressure trend data; then, defines the time window of the current respiratory cycle, calculates the average value of the intracranial pressure in the window as the amplitude, and then uses the sine function to simulate the respiratory waveform. In the current time window, the value of the respiratory rate vector is set to the set respiratory rate; the time pointer and respiratory cycle counter are updated to process the next respiratory cycle; finally, the generated respiratory signal and respiratory rate vector are returned. The respiration_signal function uses the average value of the intracranial pressure signal in the window as the amplitude to ensure that the simulated respiratory waveform matches the actual intracranial pressure change. The sine function can well simulate the periodic changes in the respiratory process and reflect the impact of breathing on intracranial pressure.

[0078] In the above embodiment, based on each heartbeat cycle, the heartbeat interval, the pulse amplitude and shape of the intracranial pressure change within the heartbeat cycle are adjusted to simulate the instantaneous pressure change of intracranial pressure caused by the heartbeat, and the intracranial pressure change pulse signal of each heartbeat cycle is obtained, including: based on each heartbeat cycle, the heartbeat interval, the pulse amplitude and shape are adjusted, and the instantaneous pressure change of intracranial pressure caused by the heartbeat is simulated by using the generateICPpulse function to obtain the intracranial pressure change pulse signal of each heartbeat cycle.

[0079] Specifically, the generateICPpulse function first initializes an array to store the intracranial pressure change pulse signal and arrhythmia flag of the heartbeat cycle; at the same time, the initial heartbeat interval value is determined based on the given sampling frequency; the arrhythmia phenomenon caused by breathing is simulated by adjusting the sine waveform, and then the actual heartbeat interval of each heartbeat cycle is calculated; the pulse signal that conforms to the physiological characteristics is generated by gamma distribution. In this step, the pulse amplitude is dynamically adjusted according to the average intracranial pressure in the current time window to ensure the accuracy of the simulation; the generated pulse signal is appropriately scaled and linearly interpolated to adapt to different heartbeat intervals to ensure the continuity and physiological rationality of the signal. The generateICPpulse function can more accurately reflect the physiological changes in actual conditions by introducing respiratory frequency and arrhythmia factors. By using gamma distribution to generate pulse signals, it can not only simulate normal heart activity well, but also effectively deal with arrhythmia conditions, thereby improving the scope of application of the model.

[0080] In the above embodiment, when the extraction / injection process at each time point is simulated according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and the intracranial pressure change signal during the corresponding extraction / injection process is generated, the simulate_treatment function is used to simulate the extraction / injection process at each time point according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and the intracranial pressure change signal during the corresponding extraction / injection process is generated.

[0081] Specifically, the simulate_treatment function first initializes the current intracranial pressure, intracranial volume, elastic coefficient of the cranial cavity, baseline volume and nonlinearity, sets the injection rate, extraction rate, and volume of solid and liquid hematomas; then, it decides whether to perform injection or extraction based on the intracranial pressure and the volume of the hematoma. When the intracranial pressure is lower than a certain threshold (for example, 15 mmHg), the hematoma is considered to be successfully cleared and the treatment is over. When the intracranial pressure is higher than the threshold and there is liquid hematoma, extraction is performed. When the liquid hematoma is less than a certain threshold (for example, 0.2 ml), an injection operation is performed to liquefy the solid hematoma; by introducing the degree of nonlinearity, the function can more accurately simulate the intracranial pressure-volume relationship and reflect the complexity under real physiological conditions. Calculate the change in intracranial pressure and update the current intracranial pressure, and finally return the change in intracranial pressure at each time point during the treatment process.

[0082] In addition, in the above embodiment, in order to improve the realism of the simulation signal, when performing the intracranial pressure simulation in the intracranial hematoma suction environment, the total intracranial pressure signal may be subjected to noise distortion and filtering processing to obtain the final intracranial pressure signal.

[0083] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail from the perspective of technical principles and calculations, as follows:

[0084] like Figure 3 - Fig. 9 As shown, the Ursino-Lodi physiological model is a physiological model of cerebral blood circulation. The model combines biophysics, brain metabolic biochemistry and vascular smooth muscle function models, and mathematically constructs the dynamic balance and feedback mechanism of cerebral blood flow. Taking into account the blood flow characteristics from the aorta to the blood vessels and then to the veins, as well as their indirect effects on cerebral blood flow, the model simulates the automatic regulation mechanism of cerebral blood flow to changes in blood pressure to maintain a constant blood flow, and even when the blood pressure fluctuates, it can maintain the stability of cerebral perfusion and analyze changes in intracranial pressure. The Ursino-Lodi physiological model is written in python. First, the simulation parameters and time step are set, and a time array is created based on the total simulation time and sampling frequency. Then the model parameters are set, including coefficients and variables related to vascular resistance, arterial blood pressure, intracranial blood flow and cerebrospinal fluid dynamics. Multiple vectors are created for simulation calculations, such as cerebrospinal fluid volume, intracranial pressure, cerebral arterial pressure, etc. In the physiological model, the arterial exchange area needs to be calculated. and rate of change of intracranial pressure.

[0085] Among them, arterial exchange area The calculation is given by formula (1-1):

[0086] ;

[0087] In the formula, represents the arterial exchange area, represents the value of the kth time step, represents the arterial compliance at the kth time, represents the arterial pressure at the kth time, represents the intracranial pressure at the kth time.

[0088] Arterial resistance The calculation is given by formula (1-2):

[0089] ;

[0090] In the formula, Represents arterial resistance, represents the compliance coefficient, Indicates normal arterial compliance, represents arterial exchange area.

[0091] Cerebral perfusion pressure The calculation is given by formula (1-3):

[0092] ;

[0093] In the formula, represents cerebral perfusion pressure, Indicates venous resistance, represents the arterial pressure at the kth time, represents the intracranial pressure at the kth time, Represents arterial resistance.

[0094] Blood flow The calculation is given by formula (1-4):

[0095] ;

[0096] In the formula, Indicates blood flow, represents the arterial pressure at the kth time; represents the cerebral perfusion pressure at the kth time; Represents arterial resistance.

[0097] Then define a dimensionless variable is the nominal flow, the change in arterial compliance depending on The value of is given by formula (1-5):

[0098] ;

[0099] In the formula, and They represent the changes in arterial compliance under different conditions, specifically: Represents changes in arterial compliance under low blood flow conditions, when blood flow falls below a certain threshold Blood vessels may exhibit greater compliance, or the ability to expand more at lower pressures, to maintain adequate blood flow. It represents the change in arterial compliance under normal or high blood flow conditions. When the blood flow is higher, the compliance of the blood vessels will decrease accordingly to prevent excessive expansion and maintain the structural integrity of the blood vessels.

[0100] Then the sigmoid function is used to simulate the nonlinear change of arterial compliance, which is given by (1-6):

[0101] ;

[0102] In the formula , which means that the output of formula (1-6) is used to calculate the rate of change of arterial compliance; Represents the slope, and its value is ; is the gain factor, Indicates normal arterial compliance; is the arterial resistance defined by formula (1-2); x is the dimensionless variable defined above.

[0103] Rate of change of arterial compliance From (1-7) we get:

[0104] ;

[0105] In the formula, represents the time constant, represents the arterial compliance at the kth time.

[0106] Rate of change of intracranial pressure From formula (1-8), we get:

[0107] ;

[0108] In the formula, Indicates the rate of cerebrospinal fluid production; represents the time derivative of arterial pressure, represents the resistance to cerebral blood flow, The resistance to cerebrospinal fluid outflow. Indicates the venous sinus pressure, represents the volume of cerebrospinal fluid injected, represents the rate of change of arterial compliance at the kth time; is the intracranial pressure at the kth time defined by formula (1-1); is the arterial pressure at the kth time defined by formula (1-1); is the cerebral perfusion pressure at the kth time defined by formula (1-3).

[0109] The differential equations are solved using the fourth-order Runge–Kutta method to update the arterial exchange area and intracranial pressure, and then the cerebrospinal fluid volume. From formula (1-9), we get:

[0110] ;

[0111] In the formula, Indicates The volume of cerebrospinal fluid at that time, Indicates The volume of cerebrospinal fluid at that time, Indicates intracranial pressure at a time; express ICP compliance over time; Indicates the time step, which is the interval between each time point in the simulation process. Finally, the intracranial pressure physiological model module returns the simulation time vector, intracranial pressure , arterial exchange area and cerebrospinal fluid volume result.

[0112] As for the effect of heartbeat on intracranial pressure changes, in practical applications, the array used to store the heart pulse signal is initialized, and a flag is set to indicate whether arrhythmia is considered. According to the set sampling frequency, the value of the initial heartbeat interval is determined. By adjusting the heartbeat interval signal of the sine waveform, the sinus arrhythmia caused by breathing is simulated. Then, the heart pulse signal is generated in a loop process. In each loop, the current heartbeat interval is calculated and the time window of each pulse is determined. , given by (1-10):

[0113] ;

[0114] Where t represents the time point of respiratory sampling, is the respiratory rate, is the respiratory rate, and the pulse amplitude is calculated based on the exponential pressure-volume relationship , given by (1-11):

[0115] ;

[0116] In the formula, Indicates the average intracranial pressure in the current time window and defines the parameters of the gamma distribution to generate a pulse of a specific shape; e represents the base of the natural logarithm. When dealing with premature beats, the heartbeat interval and pulse shape are adjusted as needed, and the corresponding pulse signal is generated using the gamma distribution. , given by (1-12):

[0117] ;

[0118] In the formula, Represents the horizontal coordinate value used to calculate the probability density function of the gamma distribution, is the pulse amplitude, represents the probability density function of the i-th gamma distribution, is the weight of the ith pulse, is the displacement of the ith pulse, and the denominator is used to normalize the pulse amplitude.

[0119] By scaling and interpolating the pulse signals to fit the current heartbeat interval, these signals are inserted into the icpBeats array, which aims to integrate the pulse signals of each heartbeat cycle to form a complete ICP signal. The scaling is given by (1-13):

[0120] ;

[0121] In the formula, is a sequence of sampling points that defines the pulse signal, Is a sequence range, is the length of the x sequence, is the heart beat interval, Defined as a new sequence whose length is consistent with IBI, retaining the original sequence The relative spacing ratio ensures that the shape of the pulse signal remains unchanged after scaling.

[0122] Linear interpolation is used to convert the pulse signal from The sequence is resampled to Define the heartbeat interval length, map the sampling points of the pulse signal from the original definition domain to the new length, ensure that the shape and characteristics of the signal are preserved after scaling, and the linear interpolation is given by (1-14):

[0123] ;

[0124] In the formula, , the linear interpolation formula is used to estimate the unknown value between two known data points, thus completing the scaling and interpolation process of the pulse signal; are two known data points and a new sampling point Need to calculate the corresponding value; The corresponding , Then the corresponding , is the current index, correspond , Then the corresponding , use the linear interpolation method to resample the pulse signal Pulse to adapt to the current heartbeat interval. Finally, update the time pointer and pulse counter, and return the generated heart pulse signal array icpBeats, which is a signal representing the change in intracranial pressure. From (1-10) to (1-14) is the specific implementation method of the generateICPpulse function.

[0125] For the influence of breathing on intracranial pressure changes, in practical applications, set the breathing frequency, initialize the time pointer and the array storing the breathing signal. Create a breathing frequency vector with the same length as the intracranial pressure trend data, and initialize the breathing cycle counter. Define the time window of the current breathing cycle, which is given by (1-15):

[0126] ;

[0127] In the formula, Indicates the time pointer. represents the sampling frequency, Respiratory rate, Indicates how many sampling points there are in a complete breathing cycle. The round function is used to convert The result is rounded to the nearest integer to ensure that the number of sampling points in each respiratory cycle is an integer, which is convenient for use when generating respiratory signals. Then the time window of the current respiratory cycle and the amplitude of the signal within the window are calculated. , given by (1-16):

[0128] ;

[0129] In the formula, Represents the average value of the ICP signal within the window, and then uses a sine function to simulate the respiratory waveform , given by (1-17):

[0130] ;

[0131] Where t represents the time point of respiratory sampling, Respiratory rate, is the pulse amplitude.

[0132] Then, the respiration frequency vector is updated so that its value in the current window is the set respiration frequency. The time pointer and respiration cycle counter are updated, and the generated respiration signal and respiration frequency vector are returned. From (1-15) to (1-17) is the specific implementation method of the respiration_signal function.

[0133] Regarding the effect of hematoma aspiration on intracranial pressure, the intracranial pressure trend, the injection rate, the extraction rate, and the volume of solid and liquid hematomas are used to determine whether to perform injection or extraction at each time point. In the present invention, when the intracranial pressure is lower than When the hematoma is successfully evacuated, the treatment is over. When there is liquid hematoma, the fluid is drained. Otherwise, when the liquid hematoma is less than a threshold, the solid hematoma is injected to liquefy the solid hematoma. The threshold here can be set to 0.2 ml. The corresponding intracranial pressure change is calculated, which is given by formula (1-18):

[0134] ;

[0135] In the formula, Indicates the time point of hematoma aspiration operation, Indicates the current intracranial pressure, Indicates the intracranial volume, Indicates the injection rate ( ) and suction rate ( ), represents the elastic coefficient of the cranial cavity, represents the baseline volume, Indicates the degree of nonlinearity of the intracranial pressure-volume relationship. It indicates that the rate at which intracranial pressure increases with volume is faster than a linear relationship, indicating that intracranial pressure is increasingly sensitive to changes in volume, and any additional increase in volume will result in a large increase in pressure; It indicates that the intracranial pressure-volume relationship is linear, meaning that the increase in pressure is proportional to the increase in volume, which rarely occurs under real physiological conditions; It indicates that the rate at which intracranial pressure increases with volume is slower than the linear relationship, corresponding to a higher compliance of the cranial cavity or a weaker response of intracranial pressure to volume changes under certain conditions. Formula (1-18) is the specific implementation method of the simulate_treatment function.

[0136] In order to improve the realism of the simulated signal, noise can be added to the given intracranial pressure signal and the signal distortion can be processed. Specifically, the low-pass filtering method is first used to remove high-frequency noise and unnecessary high-frequency components in the signal while retaining the low-frequency information in the signal. A signal subsegment is selected and a 3rd-order Butterworth low-pass filter is designed using the signal.butter function in Python's scipy library (an advanced scientific computing library). The cutoff frequency is set to is the sampling frequency. Signal components above this frequency will be significantly attenuated, while signal components below this frequency can pass through the filter relatively completely. The high-frequency components of a specific sub-segment in the original signal are effectively removed, and the low-frequency components are retained, thereby realizing low-pass filtering of the signal. Then, hard saturation is used to simulate signal loss, locate the starting point of signal loss, and then determine the duration of signal loss. Setting the signal value to 0 simulates the situation where the signal is completely lost. Finally, progressive saturation is used to simulate the situation where the signal dynamic range is limited or faces nonlinear distortion. The starting point of the signal gradient is located, the duration of the signal gradient is determined, and the hyperbolic tangent function is applied to smoothly transition from the initial signal value to the saturation value. This gradual processing helps to test and optimize signal processing, and can effectively evaluate the robustness and recovery ability of the algorithm.

[0137] The present invention is used to simulate the change of intracranial pressure, and combines multiple physiological signals to generate a comprehensive intracranial pressure signal. It mainly includes the long-term trend of the physiological model, respiratory signal, heartbeat signal, hematoma suction signal and noise processing. Initialize the parameters, set the sampling frequency and create a time vector, use the intracranial pressure physiological model module to simulate the intracranial pressure trend, use the respiratory module to simulate the respiratory signal, use the heartbeat module to simulate the single heartbeat signal, and use the hematoma suction module to simulate the hematoma signal. Then synthesize the comprehensive intracranial pressure signal, add noise processing to the intracranial pressure signal and then return the adjusted intracranial pressure signal. By integrating the above modules, the present invention can provide a comprehensive intracranial pressure signal that simulates the long-term trend of intracranial pressure changes, respiratory and heartbeat signals, and the impact of hematoma treatment, providing a simulation basis for the formulation of intracranial hematoma removal treatment plans and effect evaluation.

[0138] Fig.10 An embodiment of a computer device of the present invention is shown, which may be a server, and includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0139] Those skilled in the art will understand that Fig.10The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0141] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0142] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0143] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for simulating intracranial pressure in an intracranial hematoma suction environment, characterized in that: include: Constructing an intracranial pressure physiological model and collecting intracranial pressure physiological parameters of patients; generating an intracranial pressure physiological vector according to the intracranial pressure physiological parameters of patients and based on the intracranial pressure physiological model; simulating intracranial pressure changes at each time point using a Sigmoid function according to the intracranial pressure physiological vector, and obtaining a corresponding time window and intracranial pressure change trend signal; wherein the intracranial pressure physiological parameters include cerebral blood volume, vascular resistance, arterial blood pressure, intracranial blood flow coefficient and variables, cerebrospinal fluid dynamic coefficient and variables, and the intracranial pressure physiological vector includes cerebrospinal fluid volume, intracranial pressure, and arterial exchange area; The time window is used as a respiratory cycle window, the respiratory frequency and shape are adjusted based on the respiratory cycle window, the respiration_signal function is used to simulate the intracranial pressure change, and a respiratory pulse signal of the same length as the intracranial pressure change trend signal is generated; wherein the respiration_signal function first sets the respiratory frequency and respiratory cycle counter, initializes the time pointer and the array for storing the respiratory signal, and creates a respiratory frequency vector with the same length as the intracranial pressure trend data; then, the time window of the current respiratory cycle is defined, the average value of the intracranial pressure in the window is calculated as the amplitude, and then the sine function is used to simulate the respiratory waveform; within the current time window, the value of the respiratory frequency vector is set to the set respiratory frequency; the time pointer and the respiratory cycle counter are updated to process the next respiratory cycle; finally, the generated respiratory signal and respiratory frequency vector are returned; Based on each heartbeat cycle, the heartbeat interval, pulse amplitude and shape are adjusted, and the generateICPpulse function is used to simulate the instantaneous intracranial pressure change caused by the heartbeat to obtain the intracranial pressure change pulse signal of each heartbeat cycle, and the intracranial pressure change pulse signals of each heartbeat cycle are integrated to obtain the intracranial pressure pulse sequence; wherein, the generateICPpulse function first initializes an array for storing the intracranial pressure change pulse signal of the heartbeat cycle and the flag bit of arrhythmia, and at the same time determines the initial heartbeat interval value based on the given sampling frequency, and simulates the arrhythmia phenomenon caused by breathing by adjusting the sine waveform, and then calculates the actual heartbeat interval of each heartbeat cycle, and uses the gamma distribution to generate a pulse signal that conforms to the physiological characteristics; According to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, the simulate_treatment function is used to simulate the extraction / injection process at each time point to generate the corresponding intracranial pressure change signal during the extraction / injection process; wherein, the simulate_treatment function first initializes the current intracranial pressure, intracranial volume, elastic coefficient of the cranial cavity, baseline volume and nonlinearity, sets the injection rate, extraction rate, solid and liquid hematoma volumes, and then decides whether to perform injection or extraction according to the intracranial pressure and hematoma volume; The intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal are integrated to obtain a total intracranial pressure signal.

2. The method for simulating intracranial pressure in an intracranial hematoma suction environment according to claim 1, characterized in that: The intracranial pressure physiological model is the Ursino-Lodi physiological model.

3. The method for simulating intracranial pressure in an intracranial hematoma suction environment according to claim 1, characterized in that: Also includes: The total intracranial pressure signal is subjected to noise distortion and filtering processing to obtain a final intracranial pressure signal.

4. A system for simulating intracranial pressure in an intracranial hematoma suction environment, characterized in that: include: The intracranial pressure-physiological simulation module is used to construct an intracranial pressure physiological model and collect the patient's intracranial pressure physiological parameters; according to the patient's intracranial pressure physiological parameters and based on the intracranial pressure physiological model, an intracranial pressure physiological vector is generated; according to the intracranial pressure physiological vector, a Sigmoid function is used to simulate the intracranial pressure change at each time point to obtain the corresponding time window and intracranial pressure change trend signal; wherein, the intracranial pressure physiological parameters include: cerebral blood volume, vascular resistance, arterial blood pressure, intracranial blood flow coefficient and variables, cerebrospinal fluid dynamic coefficient and variables; the intracranial pressure physiological vector includes: cerebrospinal fluid volume, intracranial pressure, and arterial exchange area; The intracranial pressure-respiration simulation module is used to use the time window as a respiratory cycle window, adjust the respiratory frequency and shape based on the respiratory cycle window, use the respiration_signal function to simulate the intracranial pressure change, and generate a respiratory pulse signal of the same length as the intracranial pressure change trend signal; wherein the respiration_signal function first sets the respiratory frequency and respiratory cycle counter, initializes the time pointer and the array for storing the respiratory signal, and creates a respiratory frequency vector with the same length as the intracranial pressure trend data; then, defines the time window of the current respiratory cycle, calculates the average value of the intracranial pressure in the window as the amplitude, and then uses the sine function to simulate the respiratory waveform; within the current time window, sets the value of the respiratory frequency vector to the set respiratory frequency; updates the time pointer and the respiratory cycle counter to process the next respiratory cycle; finally, returns the generated respiratory signal and respiratory frequency vector; The intracranial pressure-heartbeat simulation module is used to adjust the heartbeat interval, pulse amplitude and shape based on each heartbeat cycle, and use the generateICPpulse function to simulate the instantaneous intracranial pressure change caused by the heartbeat to obtain the intracranial pressure change pulse signal of each heartbeat cycle, and integrate the intracranial pressure change pulse signals of each heartbeat cycle to obtain the intracranial pressure pulse sequence; wherein, the generateICPpulse function first initializes an array to store the intracranial pressure change pulse signal of the heartbeat cycle and the arrhythmia flag, and at the same time determines the initial heartbeat interval value based on the given sampling frequency, and simulates the arrhythmia phenomenon caused by breathing by adjusting the sine waveform, and then calculates the actual heartbeat interval of each heartbeat cycle, and uses the gamma distribution to generate a pulse signal that conforms to the physiological characteristics; The intracranial pressure-hematoma simulation module is used to simulate the extraction / injection process at each time point using the simulate_treatment function according to the predetermined hematoma volume, extraction / injection rate and intracranial pressure change trend signal, and generate the corresponding intracranial pressure change signal during the extraction / injection process; wherein the simulate_treatment function first initializes the current intracranial pressure, intracranial volume, elastic coefficient of the cranial cavity, baseline volume and nonlinearity, sets the injection rate, extraction rate, solid and liquid hematoma volume, and then decides whether to perform injection or extraction according to the intracranial pressure and hematoma volume; The intracranial pressure signal integration module is used to integrate the intracranial pressure change trend signal, the respiratory pulse signal, the intracranial pressure pulse sequence and the intracranial pressure change signal to obtain a total intracranial pressure signal.

5. The intracranial pressure simulation system for intracranial hematoma suction environment according to claim 4 is characterized in that: The intracranial pressure physiological model is the Ursino-Lodi physiological model.

6. The intracranial pressure simulation system for intracranial hematoma suction environment according to claim 4 is characterized in that: Also includes: The signal processing module is used to perform noise distortion and filtering processing on the total intracranial pressure signal to obtain a final intracranial pressure signal.

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