Hospital epilepsy disease monitoring method and system and storage medium

Abstract

The invention discloses a hospital epilepsy disease monitoring method and system and a storage medium, and relates to the technical field of brain data analysis, and the method comprises the steps: obtaining the body related data of a patient through a data collection module, and carrying out the brain state evaluation calculation of the body related data of the patient through a data processing module, and obtaining a brain state evaluation coefficient; a brain state evaluation threshold value is set through a data analysis module, ratio calculation is carried out on the brain state evaluation coefficient and the brain state evaluation threshold value, and whether the brain of the patient has an abnormal problem or not is judged according to a ratio calculation result; if abnormity exists, the type division module obtains the electroencephalogram of the patient, the epilepsy type is determined by comparing the parameter size of the waveform of the electroencephalogram of the patient with the preset range, and then the execution module is used for prompting so as to monitor the epilepsy disease of the patient, so that whether the brain of the patient is abnormal or not can be determined; and judging the epilepsy type grade of the brain of the abnormal patient.

Description

Technical Field

[0001] The present invention relates to the technical field of brain data analysis, and specifically to a method, system and storage medium for monitoring epilepsy diseases in hospitals. Background Art

[0002] In the medical field, many brain diseases (such as epilepsy) cannot be treated reasonably, timely and effectively, and there is a large "treatment gap". The corresponding research faces the following challenges: Difficulties in scientific breakthroughs: Multidisciplinary integration is required, high-value data is difficult to obtain, and the data has not been effectively preprocessed, resulting in brain diseases such as epilepsy not being detected and treated in time, causing patients to miss the best treatment opportunity. Summary of the Invention

[0003] To solve the deficiencies mentioned in the above background art, the purpose of the present invention is to provide a method, system and storage medium for monitoring epilepsy diseases in hospitals, which can determine whether the patient's brain is abnormal and judge the epilepsy type level of the abnormal patient's brain.

[0004] In a first aspect, the purpose of the present invention can be achieved through the following technical solutions: A hospital epilepsy disease monitoring system, including:

[0005] A data processing module: used to obtain the patient's body-related data collected by the data acquisition module, preprocess and identify the patient's body-related data, and use the identified patient's body-related data to calculate the brain state assessment, obtaining a brain state assessment coefficient;

[0006] A data analysis module: used to set a brain state assessment threshold, calculate the ratio of the brain state assessment threshold to the brain state assessment coefficient in the data processing module, and determine whether there is an abnormality in the patient's brain according to the ratio result. If not, send a normal signal to the execution module, otherwise send a type classification signal to the type classification module;

[0007] A type classification module: used to obtain the patient's electroencephalogram, determine different epilepsy types based on the comparison of the parameter size of the patient's electroencephalogram waveform with the set range, and send different epilepsy type signals to the execution module based on the divided different epilepsy types;

[0008] An execution module: used to prompt that the patient's brain is normal after receiving the normal signal, and prompt the type level of the patient's epilepsy after receiving various different epilepsy signals.

[0009] Combined with the first aspect, in some implementation manners of the first aspect, the system further includes: The patient's body-related data of the data acquisition module includes cerebrospinal fluid data, liver function-related data, and kidney function-related data.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the data processing module performs identification processing on the processed patient body-related data: wherein, the cerebrospinal fluid data is marked as Jx, the liver function-related data is marked as Gx, and the kidney function-related data is marked as Sx, where x is the collection times label of the data collection module, and x = 1, 2, 3,..., y, and y is the total number of data collection times of the data collection module.

[0011] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the calculation process of the data processing module:

[0012] Using the formula calculate the brain state evaluation coefficient Nx, where J0 is a preset cerebrospinal fluid ratio-related coefficient, G0 is a preset liver function ratio-related coefficient, S0 is a preset kidney function ratio-related coefficient, q1 is a liver function influence coefficient, q2 is a kidney function influence coefficient, and q3 is a cerebrospinal fluid influence coefficient.

[0013] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the analysis process of the data analysis module:

[0014] Set the brain state evaluation threshold N0;

[0015] Using the formula calculate the ratio result Bx, where α and β are preset proportionality coefficients, and α + β = 1, and compare the ratio result Bx with the preset ratio threshold B0:

[0016] If Bx ≥ B0, send a normal signal to the execution module;

[0017] If Bx < B0, send a type classification signal to the type classification module.

[0018] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: the classification process of the type classification module:

[0019] Set the frequency of the patient's electroencephalogram waveform as P and the amplitude of the patient's electroencephalogram waveform as F; set the first frequency range [P1min, P1max], the first amplitude range [F1min, F1max], the second frequency range [P2min, P2max], and the second amplitude range [F2min, F2max]; where, P2min > P1max and F2min > F1max;

[0020] If P1min ≤ P ≤ P1max and F1min ≤ F ≤ F1max, send a non-epilepsy signal to the execution module;

[0021] If P1min ≤ P ≤ P1max and F2min ≤ F ≤ F2max, then send a first type of epilepsy signal to the execution module;

[0022] If P2min ≤ P ≤ P2max and F1min ≤ F ≤ F1max, then send a second type of epilepsy signal to the execution module;

[0023] If P2min ≤ P ≤ P2max and F2min ≤ F ≤ F2max, then send a third type of epilepsy signal to the execution module.

[0024] In a second aspect, to achieve the above object, the present invention discloses a method for monitoring epilepsy diseases in a hospital, and the method includes the following steps:

[0025] Obtain the patient's body-related data, perform a brain state evaluation calculation on the patient's body-related data, and obtain a brain state evaluation coefficient;

[0026] Set a brain state evaluation threshold, perform a ratio calculation on the brain state evaluation coefficient and the brain state evaluation threshold, and determine whether there are any abnormal problems in the patient's brain according to the ratio calculation result;

[0027] If there is an abnormality, obtain the patient's electroencephalogram, and determine the epilepsy type based on the comparison between the parameter size of the patient's electroencephalogram waveform and a preset range, so as to monitor the patient's epilepsy disease.

[0028] In another aspect of the present invention, to achieve the above object, a computer-readable storage medium is disclosed. A computer program is stored in the computer-readable storage medium, and when the computer program is loaded and executed by a processor, the above-mentioned hospital epilepsy disease monitoring system is adopted.

[0029] Advantages of the present invention:

[0030] The present invention obtains the patient's body-related data through the data acquisition module, uses the data processing module to perform a brain state evaluation calculation on the patient's body-related data, and obtains a brain state evaluation coefficient; and sets a brain state evaluation threshold through the data analysis module, performs a ratio calculation on the brain state evaluation coefficient and the brain state evaluation threshold, and determines whether there are any abnormal problems in the patient's brain according to the ratio calculation result; if there is an abnormality, the type classification module obtains the patient's electroencephalogram, determines the epilepsy type based on the comparison between the parameter size of the patient's electroencephalogram waveform and a preset range, and then gives a prompt through the execution module to monitor the patient's epilepsy disease, realizing the function of being able to determine whether the patient's brain is abnormal and judging the epilepsy type level of the abnormal patient's brain. Description of the Drawings

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings;

[0032] Figure 1 is a schematic structural diagram of the system of the present invention;

[0033] Figure 2 is a schematic flowchart of the method of the present invention. Specific embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0035] Embodiment 1:

[0036] The following introduces the relevant terms involved in the embodiments of the present application:

[0037] Epilepsy is a chronic brain disease characterized by recurrent epileptic seizures. It is caused by abnormal discharges of brain neurons, and the seizures of the disease are characterized by recurrence and transience. The causes of epilepsy include muscle contraction, cerebral cortical dysplasia, brain tumors, head trauma, central nervous system infections, etc., and may be related to genetics. The onset of epilepsy is not limited to any age group, and children and the elderly are relatively common. According to statistics, epilepsy affects more than 70 million people globally, and the incidence rate in China is between 5‰ and 7‰, with about 400,000 to 600,000 people newly diagnosed with epilepsy every year.

[0038] As Figure 1 shown, the hospital epilepsy disease monitoring system includes:

[0039] a data acquisition module, a data processing module, a data analysis module, a type classification module, and an execution module;

[0040] The data acquisition module is used to acquire patient body-related data and send the acquired patient body-related data to the data processing module for processing. Among them, the patient body-related data includes cerebrospinal fluid data, liver function-related data, and kidney function-related data;

[0041] After receiving the patient body-related data sent by the data acquisition module, the data processing module performs data processing. Specifically, the processing process of the data processing module includes the following steps:

[0042] Preprocess the patient's body-related data to obtain the processed patient's body-related data. Perform identification processing on the processed patient's body-related data to obtain the identified patient's body-related data. Use the identified patient's body-related data to calculate the brain state assessment, and obtain the brain state assessment coefficient. The specific process is as follows:

[0043] Preprocessing the patient's body-related data includes: missing value processing, outlier processing, and deduplication;

[0044] Among them, in the specific implementation process, missing value processing includes: Deletion: When the amount of missing data is small, directly delete the missing records. Filling: Fill with the mean, median (for numerical data), mode (for categorical variables), or use interpolation (such as time series) or model prediction (such as KNN). Marking: Mark the missing as a special value (such as marking with NaN).

[0045] Outlier processing: Detection: Use box plots (IQR method), Z-score (>3σ is an outlier), DBSCAN clustering, etc. Processing: Delete, replace with upper and lower limits, or keep (such as real high-income data).

[0046] Deduplication: Delete completely duplicate records;

[0047] Perform identification processing on the processed patient's body-related data: Among them, mark the cerebrospinal fluid data as Jx, mark the liver function-related data as Gx, and mark the kidney function-related data as Sx, where x is the acquisition times label of the data acquisition module, and x = 1, 2, 3,..., y, and y is the total number of acquisition times of the data acquisition module;

[0048] Use the formula Calculate the brain state assessment coefficient Nx. In the formula, J0 is the preset cerebrospinal fluid ratio-related coefficient, G0 is the preset liver function ratio-related coefficient, S0 is the preset kidney function ratio-related coefficient, q1 is the liver function influence coefficient, q2 is the kidney function influence coefficient, and q3 is the cerebrospinal fluid influence coefficient;

[0049] Furthermore, in the specific implementation process, the preset cerebrospinal fluid ratio-related coefficient, the preset liver function ratio-related coefficient, and the preset kidney function ratio-related coefficient are obtained by collecting cerebrospinal fluid data, liver function-related data, and kidney function-related data daily, through multiple simulation calculations and taking the data mean, and finally obtaining the ratio according to multiple data;

[0050] Among them, within this embodiment, the liver function influence coefficient, the kidney function influence coefficient, and the cerebrospinal fluid influence coefficient are calculated through comprehensive evaluation according to external factor influences when the present application obtains cerebrospinal fluid data, liver function-related data, and kidney function-related data on a daily basis, including human factors, machine detection, and environmental factors, etc.; human factors are caused by some human operations or improper scanning; environmental factors include the influence of human physique and physical environment;

[0051] The data processing module sends the calculated brain state evaluation coefficient Nx to the data analysis module for data analysis. After receiving the brain state evaluation coefficient Nx sent by the data processing module, the data analysis module conducts data analysis. Specifically, the analysis process of the data analysis module includes the following steps:

[0052] Set a brain state evaluation threshold N0, calculate the ratio of the calculated brain state evaluation coefficient Nx to the brain state evaluation threshold N0, and determine whether there are abnormal problems in the patient's brain according to the ratio calculation result. The specific process is as follows:

[0053] Use the formula Calculate the ratio result Bx. In the formula, α and β are preset proportionality coefficients, and α + β = 1. Compare the ratio result Bx with the preset ratio threshold B0:

[0054] If Bx ≥ B0, it is determined that the patient's brain is normal at this time, and the data analysis module sends a normal signal to the execution module;

[0055] If Bx < B0, it is determined that there may be epilepsy abnormalities in the patient's brain at this time, and the data analysis module sends a type classification signal to the type classification module;

[0056] After receiving the type classification signal sent by the data analysis module, the type classification module classifies the patient's epilepsy type. Specifically, the classification process of the type classification module includes the following steps:

[0057] Obtain the patient's electroencephalogram, and determine the epilepsy type based on the comparison between the parameter size of the patient's electroencephalogram waveform and the set range, so as to classify the patient's epilepsy type, and send different types of epilepsy signals to the execution module based on the different classified types:

[0058] Set the frequency of the patient's electroencephalogram waveform as P, and set the amplitude of the patient's electroencephalogram waveform as F; set the first frequency range [P1min, P1max], the first amplitude range [F1min, F1max], the second frequency range [P2min, P2max], and the second amplitude range [F2min, F2max]; where, P2min > P1max, F2min > F1max;

[0059] If P1min ≤ P ≤ P1max and F1min ≤ F ≤ F1max, it is determined that there are no epileptic symptoms at this time, and the type classification module sends a no-epilepsy signal to the execution module;

[0060] If P1min ≤ P ≤ P1max and F2min ≤ F ≤ F2max, it is determined that this is the first type of epilepsy, and the type classification module sends a first-type epilepsy signal to the execution module;

[0061] If P2min ≤ P ≤ P2max and F1min ≤ F ≤ F1max, it is determined that this is the second type of epilepsy, and the type classification module sends a second-type epilepsy signal to the execution module;

[0062] If P2min ≤ P ≤ P2max and F2min ≤ F ≤ F2max, it is determined that this is the third type of epilepsy, and the type classification module sends a third-type epilepsy signal to the execution module;

[0063] In this embodiment, the higher the level number of the epileptic signal of a certain type, the greater the frequency and amplitude of the brain wave, indicating that the epileptic condition is more serious at this time; the first type of epilepsy can be considered as focal epilepsy, and the second type of epilepsy can be considered as temporal lobe epilepsy;

[0064] After receiving the normal signal sent by the data processing module, the execution module prompts that the patient's brain is normal. After receiving various different epileptic signals sent by the type classification module, the execution module prompts the type and grade of the patient's epilepsy.

[0065] Embodiment 2: A hospital epilepsy disease monitoring method, the method includes the following steps:

[0066] Obtain the patient's body-related data, perform a brain state assessment calculation on the patient's body-related data, and obtain a brain state assessment coefficient;

[0067] Set a brain state assessment threshold, perform a ratio calculation on the brain state assessment coefficient and the brain state assessment threshold, and determine whether there are any abnormal problems in the patient's brain according to the ratio calculation result;

[0068] If there are abnormalities, obtain the patient's electroencephalogram, and determine the type of epilepsy based on the comparison between the parameter size of the patient's electroencephalogram waveform and the pre-set range, so as to monitor the patient's epilepsy disease.

[0069] Based on the same inventive concept, the present invention further provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions. Specifically, it is used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0070] It should be further noted that, based on the same inventive concept, the present invention further provides a computer storage medium, on which a computer program is stored, and the computer program, when run by a processor, executes the above method. The storage medium may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0071] The above formulas are all calculated by removing the dimension and taking their numerical values. The formula is obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through a large amount of data simulation.

[0072] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0073] The foregoing has shown and described the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure claimed.

Claims

1. Hospital epilepsy disease monitoring system, characterized in that: include: Data processing module: used to obtain the patient's body-related data collected by the data collection module, and pre-process and mark the patient's body-related data, and use the marked patient's body-related data to perform brain state evaluation calculation to obtain the brain state evaluation coefficient; Data analysis module: used to set the brain state assessment threshold, calculate the ratio of the brain state assessment threshold and the brain state assessment coefficient in the data processing module, and determine whether the patient's brain is abnormal according to the ratio result. If not, a normal signal is sent to the execution module, otherwise a type classification signal is sent to the type classification module; Type classification module: used to obtain the patient's electroencephalogram, determine different epilepsy types based on the comparison of the parameter size of the patient's electroencephalogram waveform with the set range, and send different types of epilepsy signals to the execution module based on the different epilepsy types classified; Execution module: after receiving a normal signal, it prompts the patient's brain to be normal; after receiving various types of epilepsy signals, it prompts the patient's epilepsy type and level.

2. The hospital epilepsy disease monitoring system according to claim 1, characterized in that: The patient body related data of the data acquisition module include cerebrospinal fluid data, liver function related data and kidney function related data.

3. The hospital epilepsy disease monitoring system according to claim 1, characterized in that: The data processing module performs labeling processing on the processed patient body-related data: wherein, cerebrospinal fluid data is marked as Jx, liver function-related data is marked as Gx, and kidney function-related data is marked as Sx, wherein x is the number of collection times of the data collection module, and x=1, 2, 3, ..., y, and y is the total number of collection times of the data collection module.

4. The hospital epilepsy disease monitoring system according to claim 3, characterized in that: The calculation process of the data processing module: Using the formula The brain status assessment coefficient Nx was calculated, where J0 was the preset cerebrospinal fluid ratio correlation coefficient, G0 was the preset liver function ratio correlation coefficient, S0 was the preset kidney function ratio correlation coefficient, q1 was the liver function influence coefficient, q2 was the kidney function influence coefficient, and q3 was the cerebrospinal fluid influence coefficient.

5. The hospital epilepsy disease monitoring system according to claim 1, characterized in that: The analysis process of the data analysis module: Set the brain state assessment threshold N0; Using the formula The ratio result Bx is calculated, where α and β are preset proportional coefficients, and α+β=1, and the ratio result Bx is compared with the preset ratio threshold B0: If Bx ≥ B0, a normal signal is sent to the execution module; If Bx<B0, a type classification signal is sent to the type classification module.

6. The hospital epilepsy disease monitoring system according to claim 1, characterized in that: The division process of the type division module: The frequency of the patient's electroencephalogram waveform is set to P, and the amplitude of the patient's electroencephalogram waveform is set to F; the first frequency range is set to [P1min, P1max], the first amplitude range is set to [F1min, F1max], the second frequency range is set to [P2min, P2max], and the second amplitude range is set to [F2min, F2max]; wherein P2min>P1max, F2min>F1max; If P1min≤P≤P1max, F1min≤F≤F1max, then send a no epilepsy signal to the execution module; If P1min≤P≤P1max, F2min≤F≤F2max, then send the first type of epilepsy signal to the execution module; If P2min≤P≤P2max, F1min≤F≤F1max, then send a second type of epilepsy signal to the execution module; If P2min≤P≤P2max, F2min≤F≤F2max, then the third type of epilepsy signal is sent to the execution module.

7. A method for monitoring epilepsy in a hospital, characterized in that: The method comprises the following steps: Acquire the patient's physical data, perform brain state evaluation calculation on the patient's physical data, and obtain a brain state evaluation coefficient; A brain state assessment threshold is set, a ratio calculation is performed between the brain state assessment coefficient and the brain state assessment threshold, and whether the patient's brain has abnormal problems is determined based on the ratio calculation result; If there is an abnormality, the patient's electroencephalogram is obtained, and the type of epilepsy is determined based on the comparison of the parameter size of the patient's electroencephalogram waveform with a pre-set range, thereby monitoring the patient's epilepsy.

8. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by the processor, the hospital epilepsy disease monitoring system according to any one of claims 1 to 6 is adopted.

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