A method, apparatus and processing device for configuring a syncope prediction model

Abstract

The application provides a configuration method and device of a syncope prediction model and a processing device, and creates a syncope prediction model for the disease of syncope. Through the syncope prediction model, long-term electrocardiogram data in a normal time period can be taken as input to accurately predict whether a user to be tested will have syncope and the specific syncope type, provide good data support for syncope diagnosis and treatment in the aspect of decision-making, and thus help to achieve better diagnosis and treatment. The method comprises the following steps: obtaining sample electrocardiogram data; configuring a corresponding label result for the sample electrocardiogram data, wherein the label result is used to indicate whether the user corresponding to the sample electrocardiogram data has syncope, and the label result is also used to indicate the syncope type of the user corresponding to the sample electrocardiogram data when the user has syncope, and the syncope type comprises a cardiac inhibition type, a vascular inhibition type and a mixed type; and training the syncope prediction model based on the labeled sample electrocardiogram data.

Description

Technical Field

[0001] The present application relates to the field of information, and in particular to a configuration method, device and processing equipment for a syncope prediction model. Background Art

[0002] Syncope is a common clinical condition, and the diagnosis or detection of syncope can usually be completed through the upright tilt test.

[0003] The upright tilt test is used to stimulate and diagnose vasovagal syncope by adjusting the tilt angle to make the patient passively tilt. During the specific test process, because the sensitivity of the basic tilt test is relatively low, sublingual nitroglycerin can better stimulate the symptoms and observe whether the patient has syncope through electrocardiogram. Therefore, this scheme is often used clinically to increase the diagnostic positive rate and provide objective evidence for unexplained syncope.

[0004] From the above content, it can be found that the syncope detection scheme based on the head-up tilt test has complex operating costs, and the effective detection of syncope requires medical staff to observe the patient's syncope in real time. Obviously, there are obvious limitations in detection efficiency and accuracy. Summary of the Invention

[0005] The present application provides a configuration method, device and processing equipment for a syncope prediction model, which is used to create a syncope prediction model for the disease of syncope. In this way, the syncope prediction model can use long-term electrocardiogram data within a normal time period as input to accurately predict whether the user being tested will experience syncope and the specific type of syncope, providing good data support for syncope diagnosis and treatment in decision-making, thereby helping to achieve better diagnosis and treatment.

[0006] In a first aspect, the present application provides a method for configuring a syncope prediction model, the method comprising:

[0007] Acquiring sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period;

[0008] Configuring corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope. The annotation results are also used to indicate the syncope type if the user corresponding to the sample ECG data has experienced syncope. The syncope types include cardioinhibitory, vasoinhibitory, and mixed syncope types.

[0009] Based on the labeled sample ECG data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user will experience syncope within a preset time period in the future based on the ECG data input to the model, as well as the specific syncope type if syncope occurs.

[0010] In conjunction with the first aspect of the present application, in a first possible implementation of the first aspect of the present application, in a one-dimensional model configuration scheme, the labeling result is 0 for a normal type, 1 for a cardiac inhibitory type, 2 for a vascular inhibitory type, and 3 for a mixed type;

[0011] The syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers represent the probabilities of normal type, cardiac inhibition type, vasoinhibition type and mixed type respectively.

[0012] In combination with the first possible implementation of the first aspect of the present application, in a second possible implementation of the first aspect of the present application, the sample electrocardiogram data is specifically the electrocardiogram data measured by lead II. Before training the syncope prediction model based on the labeled sample electrocardiogram data, the method further includes:

[0013] Discard the first two minutes of the sample ECG data to obtain the remaining data;

[0014] Using a sliding window approach, the remaining data is divided into multiple segments, each with a length of 7200 data points, corresponding to ten minutes. Furthermore, for each type of sample ECG data, six segments are randomly retained for each normal user, all segments are retained for each cardioinhibited user, and 24 segments are randomly retained for each vasoinhibited user and each mixed user, completing the update of the sample ECG data.

[0015] The syncope prediction model is configured to take a tensor of shape [n, 1, 72000] and a label for each sequence segment, where n is the number of sequence segments.

[0016] In combination with the second possible implementation of the first aspect of the present application, in a third possible implementation of the first aspect of the present application, the syncope prediction model is specifically a one-dimensional variant network based on VGG-16, and the model architecture of the syncope prediction model has the following configuration content:

[0017] The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling;

[0018] After the two front-to-back convolutional layers, connect a pooling layer, then continue to connect two front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then continue to connect two front-to-back fully connected layers configured with ReLU functions, and then continue with a fully connected layer configured with Softmax function.

[0019] In a fourth possible implementation of the first aspect of the present application, in the two-dimensional model configuration scheme, in the labeling result, the label assigned to the normal type is [0,0], the label assigned to the cardiac inhibition type is [1,0], the label assigned to the vascular inhibition type is [0,1], and the label assigned to the mixed type is [1,1];

[0020] The syncope prediction result of the syncope prediction model includes two predicted probability values. If the first probability value is greater than 0.5, the output is 1, and if the second predicted value is greater than 0.5, the output is 1.

[0021] In combination with the fourth possible implementation of the first aspect of the present application, in a fifth possible implementation of the first aspect of the present application, the syncope prediction model is specifically a two-dimensional variant network based on VGG-16, and the model architecture of the syncope prediction model has the following configuration content:

[0022] After the two convolutional layers with 64 convolution kernels connected front and back, connect a pooling layer, then continue to connect two convolutional layers with 128 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 256 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then continue to connect three fully connected layers with ReLU functions connected front and back, then continue with a Sigmoid function, and all convolutional layers are configured with ReLU functions.

[0023] In combination with the first aspect of the present application, in a sixth possible implementation manner of the first aspect of the present application, the method further includes:

[0024] Get the current user's electrocardiogram data;

[0025] Inputting the current user's electrocardiogram data into the syncope prediction model to perform corresponding syncope prediction processing;

[0026] extracting the syncope prediction processing result output by the syncope prediction model;

[0027] Based on the syncope prediction processing result, a syncope intervention measure reminder is output to the current user.

[0028] In a second aspect, the present application provides a device for configuring a syncope prediction model, the device comprising:

[0029] an acquiring unit, configured to acquire sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period;

[0030] a configuration unit, configured to configure corresponding annotation results for the sample electrocardiogram data, wherein the annotation results are used to indicate whether the user corresponding to the sample electrocardiogram data has experienced syncope, and the annotation results are further used to indicate the syncope type if the user corresponding to the sample electrocardiogram data has experienced syncope, wherein the syncope types include cardioinhibitory type, vasoinhibitory type, and mixed type;

[0031] The training unit is used to train a syncope prediction model based on labeled sample electrocardiogram data, wherein the syncope prediction model is used to predict whether the user will experience syncope within a preset time period in the future based on the electrocardiogram data input into the model, and the specific syncope type in the event of syncope.

[0032] In conjunction with the second aspect of the present application, in a first possible implementation of the second aspect of the present application, in a one-dimensional model configuration scheme, the labeling result is 0 for a normal type, 1 for a cardiac inhibitory type, 2 for a vascular inhibitory type, and 3 for a mixed type;

[0033] The syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers represent the probabilities of normal type, cardiac inhibition type, vasoinhibition type and mixed type respectively.

[0034] In conjunction with the first possible implementation of the second aspect of the present application, in a second possible implementation of the second aspect of the present application, the apparatus further includes a preprocessing unit configured to:

[0035] Discard the first two minutes of the sample ECG data to obtain the remaining data;

[0036] Using a sliding window approach, the remaining data is divided into multiple segments, each with a length of 7200 data points, corresponding to ten minutes. Furthermore, for each type of sample ECG data, six segments are randomly retained for each normal user, all segments are retained for each cardioinhibited user, and 24 segments are randomly retained for each vasoinhibited user and each mixed user, completing the update of the sample ECG data.

[0037] The syncope prediction model is configured to take a tensor of shape [n, 1, 72000] and a label for each sequence segment, where n is the number of sequence segments.

[0038] In conjunction with the second possible implementation of the second aspect of the present application, in a third possible implementation of the second aspect of the present application, the syncope prediction model is specifically a one-dimensional variant network based on VGG-16, and the model architecture of the syncope prediction model has the following configuration content:

[0039] The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling;

[0040] After the two front-to-back convolutional layers, connect a pooling layer, then continue to connect two front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then continue to connect two front-to-back fully connected layers configured with ReLU functions, and then continue with a fully connected layer configured with Softmax function.

[0041] In a fourth possible implementation of the second aspect of the present application, in the two-dimensional model configuration scheme, in the labeling result, the label assigned to the normal type is [0,0], the label assigned to the cardiac inhibition type is [1,0], the label assigned to the vascular inhibition type is [0,1], and the label assigned to the mixed type is [1,1];

[0042] The syncope prediction result of the syncope prediction model includes two predicted probability values. If the first probability value is greater than 0.5, the output is 1, and if the second predicted value is greater than 0.5, the output is 1.

[0043] In conjunction with the fourth possible implementation of the second aspect of the present application, in a fifth possible implementation of the second aspect of the present application, the syncope prediction model is specifically a two-dimensional variant network based on VGG-16, and the model architecture of the syncope prediction model has the following configuration content:

[0044] After the two convolutional layers with 64 convolution kernels connected front and back, connect a pooling layer, then continue to connect two convolutional layers with 128 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 256 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then continue to connect three fully connected layers with ReLU functions connected front and back, then continue with a Sigmoid function, and all convolutional layers are configured with ReLU functions.

[0045] In conjunction with the second aspect of the present application, in a sixth possible implementation manner of the second aspect of the present application, the apparatus further includes an application unit, configured to:

[0046] Get the current user's electrocardiogram data;

[0047] Inputting the current user's electrocardiogram data into the syncope prediction model to perform corresponding syncope prediction processing;

[0048] extracting the syncope prediction processing result output by the syncope prediction model;

[0049] Based on the syncope prediction processing result, a syncope intervention measure reminder is output to the current user.

[0050] In a third aspect, the present application provides a processing device comprising a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation of the first aspect of the present application is executed.

[0051] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the method provided in the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0052] From the above content, it can be concluded that this application has the following beneficial effects:

[0053] For the disease of syncope, after obtaining the sample ECG data, the present application configures corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope, and the annotation results are also used to indicate the type of syncope when the user corresponding to the sample ECG data has experienced syncope, and the syncope types include cardiac inhibition type, vascular inhibition type and mixed type. Then, based on the annotated sample ECG data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user corresponding to the ECG data input into the model will experience syncope within a preset time period in the future and the specific type of syncope when syncope occurs based on the ECG data input into the model. Under this processing mechanism, the present application has created a syncope prediction model, so that the syncope prediction model can use long-term ECG data in a normal time period as input to accurately predict whether the user being tested will experience syncope and the specific type of syncope, providing good data support for syncope diagnosis and treatment in decision-making, thereby helping to achieve better diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 A flowchart of a method for configuring a syncope prediction model according to the present application;

[0056] Figure 2 A schematic diagram of a network architecture of a one-dimensional convolutional neural network in this application;

[0057] Figure 3 A schematic diagram of a network architecture of a two-dimensional convolutional neural network for this application;

[0058] Figure 4 A schematic diagram of the structure of a configuration device for the syncope prediction model of this application;

[0059] Figure 5 This is a structural diagram of the processing equipment for this application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0061] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0062] The division of modules in this application is a logical division. In actual application, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. Moreover, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed into multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application.

[0063] Before introducing the configuration method of the syncope prediction model provided by this application, the background content involved in this application is first introduced.

[0064] The configuration method, device and computer-readable storage medium of the syncope prediction model provided in this application can be applied to processing equipment to create a syncope prediction model for the disease of syncope. In this way, the syncope prediction model can use long-term electrocardiogram data within a normal time period as input to accurately predict whether the user being tested will experience syncope and the specific type of syncope, providing good data support for syncope diagnosis and treatment in decision-making, thereby helping to achieve better diagnosis and treatment.

[0065] The syncope prediction model configuration method mentioned in this application can be executed by a syncope prediction model configuration device, or a server, physical host, or user equipment (UE) of different types of processing devices that integrate the syncope prediction model configuration device. The syncope prediction model configuration device can be implemented in hardware or software. The UE can specifically be a terminal device such as a smartphone, tablet computer, laptop computer, desktop computer, or personal digital assistant (PDA). The processing device can be set up in a device cluster.

[0066] It can be understood that the solution of the present application mainly involves the training and processing of the syncope prediction model. Generally speaking, it only requires the corresponding data processing capabilities. Therefore, the processing equipment that executes the configuration method of the syncope prediction model of the present application, or the processing equipment equipped with the application service corresponding to the configuration method of the syncope prediction model of the present application, its device type and device deployment form are relatively flexible.

[0067] If it also involves the real-time collection and processing of electrocardiogram data, or the application of syncope prediction models in clinical work, it is easy to understand that the equipment type and equipment deployment form of the processing equipment can be adaptively adjusted as the application scenario changes.

[0068] As an example, processing equipment can be configured in the form of a device cluster, which includes an electrocardiograph for collecting electrocardiogram data, a central device located in the background for training the model, and a terminal device on the doctor's side responsible for model application.

[0069] Next, the configuration method of the syncope prediction model provided by this application is introduced.

[0070] First, see Figure 1 , Figure 1 A flow chart of a method for configuring a syncope prediction model of the present application is shown. The method for configuring a syncope prediction model provided by the present application may specifically include the following steps S101 to S103:

[0071] Step S101, obtaining sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period;

[0072] It can be understood that the configuration goal of the syncope prediction model using electrocardiogram data as input may involve the configuration of training samples.

[0073] In this regard, this application can obtain sample electrocardiogram data.

[0074] Among them, the sample ECG data can be ready-made data pulled from the hospital's relevant online system, ready-made data manually entered by staff, data obtained by modification / secondary processing based on ready-made data, or directly generated data. These are all possible in actual situations.

[0075] It is worth noting that the sample ECG data is the ECG data after stripping or excluding the content related to the patient's personal privacy, and only retains the core content of the ECG related to syncope, so there is no privacy issue.

[0076] In addition, it should be noted that the sample ECG data is usually not the ECG data obtained by the head-up tilt test, but the long-term ECG data during the normal time period. It is the ECG data collected for a long time when the patient is in the usual / daily / normal state. More specifically, the state here refers to the state where the patient is not affected by the examination / treatment such as the head-up tilt test. For example, in some cases, the data of the first 10 minutes of the head-up tilt test may also meet the requirements.

[0077] Step S102, configuring corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope. The annotation results are also used to indicate the syncope type if the user corresponding to the sample ECG data has experienced syncope, and the syncope types include cardioinhibitory type, vasoinhibitory type, and mixed type.

[0078] It is understandable that for the training samples used to train the model, it is necessary to add / assign annotations / labels that the model can recognize as a true value to guide the training of the model.

[0079] In this regard, after obtaining the sample electrocardiogram data, the present application can perform annotation processing on it to configure corresponding annotation results for the sample electrocardiogram data.

[0080] During the annotation process, it can be completed manually or using corresponding automated annotation tools. The automated annotation tools involve pre-configured automated annotation logic / strategies when performing automated annotation processing. Both types of annotation methods are acceptable. Considering that the annotation processing can directly use existing technologies, no detailed explanation will be given this time.

[0081] It is worth noting that the present application specifically involves the prediction and processing of three types of syncope, namely, cardiac inhibition type, vascular inhibition type and mixed type (the mixed type has the characteristics of both cardiac inhibition type and vascular inhibition type). It can be understood that these three types of syncope are clinically clear syncope types. It can be seen that the syncope prediction model that is subsequently trained can effectively make accurate predictions of these three types of syncope in terms of details, and provide more delicate data support and decision support.

[0082] Step S103, based on the labeled sample ECG data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user will experience syncope within a preset time period in the future according to the ECG data input to the model, and the specific syncope type if syncope occurs.

[0083] It can be understood that the syncope prediction model configured in the present application is itself a machine learning model, usually a neural network model, which can use powerful artificial intelligence (AI) performance to complete the syncope prediction processing designed in the present application.

[0084] The training process based on labeled training samples, i.e., sample ECG data, usually includes the following processing:

[0085] In each round of training, a set of training samples is input into the model, so that the model can perform corresponding syncope prediction processing to realize forward propagation. Then, based on the syncope prediction results output by the model, the loss function is calculated in combination with the annotation results of the training samples, and the model parameters are optimized according to the calculation results of the loss function to realize reverse propagation. In this way, when the preset model training requirements such as prediction accuracy, number of training times or training time are met, the model training can be completed and a syncope prediction model that can be put into practical use can be obtained.

[0086] Among them, for the specific model architecture adopted (such as VGG-16 model, ResNet, etc.), model training architecture (for example, the samples can be divided into training set and validation set in a ratio of 7:3, the training set is used to train the model, and the validation set is used to verify the model training effect) and loss function, it can be understood that the existing solution can be adopted, or further optimization and improvement can be made on the basis of the existing solution, or a novel solution developed independently can be adopted. These are all possible in actual situations and can be adjusted according to actual needs.

[0087] In this way, a syncope prediction model can be obtained through the model training processing here, which is used to predict whether the user will experience syncope within a preset time period in the future and the specific type of syncope if syncope occurs, providing strong data support for clinical medical staff to understand the possibility of syncope and syncope symptoms of patients.

[0088] Corresponding to the application aspects of the model, the present application method may include:

[0089] Step S104, obtaining the electrocardiogram data of the current user;

[0090] Step S105, inputting the electrocardiogram data of the current user into the syncope prediction model to perform corresponding syncope prediction processing;

[0091] Step S106, extracting the syncope prediction processing result output by the syncope prediction model;

[0092] Step S107: Based on the syncope prediction processing result, output a syncope intervention measure reminder to the current user.

[0093] It can be understood that the input of the model in actual application, that is, the electrocardiogram data of the current user, corresponds to / aligns with the sample electrocardiogram data in terms of data format.

[0094] After the syncope prediction model performs the currently required syncope prediction processing, the syncope prediction processing result output by the model can obviously be extracted, and the syncope prediction processing result can be processed according to the preset data usage strategy.

[0095] For example, it can be stored, forwarded, and output with a prompt indicating that the processing is complete, a pre-adapted syncope diagnosis / treatment plan can be output (specifically, it can be subdivided according to the type of syncope), or further analysis and processing can be performed. These are all possible, and the specific strategy content can be configured according to the clinical work and actual situation.

[0096] For example, the present application may specifically relate to a reminder mechanism for syncope intervention measures. When syncope is predicted, the user may be directly or indirectly prompted to perform the Valsava maneuver, take a deep breath and hold it, sit down, lie down, drink water to divert attention, register or go to the emergency room, etc., in order to improve symptoms or treat the condition of syncope that has occurred or is about to occur.

[0097] Among them, corresponding to the reminder mechanism here, the processing device of this application can output reminders on the current user side in different specific reminder modes such as text, image, voice, light or vibration through its own equipment (for example, it can include monitoring instruments such as wearable devices) or through external equipment (for example, it can be connected to monitoring instruments such as wearable devices). These settings can obviously be more flexible in actual situations and can be adjusted according to actual needs. Of course, the specific reminder output can also be completed manually. The processing device only needs to give the specific reminder content required by medical staff to remind the current user of syncope intervention measures. This is also possible in actual situations.

[0098] The specific reminder content outputted may be general reminder content, reminder content adapted to the specific syncope prediction type / situation, or personalized / customized reminder content, all of which can be configured according to actual needs.

[0099] from Figure 1As can be seen from the illustrated embodiment, for the disease of syncope, after obtaining the sample ECG data, the present application configures corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope, and the annotation results are also used to indicate the syncope type of the user corresponding to the sample ECG data when syncope occurs, and the syncope types include cardiac inhibition type, vascular inhibition type and mixed type. Then, based on the annotated sample ECG data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user corresponding to the ECG data input into the model will experience syncope within a preset time period in the future and the specific syncope type when syncope occurs based on the ECG data input into the model. Under this processing mechanism, the present application has created a syncope prediction model, so that the syncope prediction model can use long-term ECG data within a normal time period as input to accurately predict whether the tested user will experience syncope and the specific syncope type, providing good data support for syncope diagnosis and treatment in decision-making, thereby helping to achieve better diagnosis and treatment.

[0100] Continue to the above Figure 1 Each step of the illustrated embodiment and its possible implementation in practical applications are described in detail.

[0101] Regarding the processing architecture of the syncope prediction model of this application, this application also provides two types of configuration schemes from the specific operation level, specifically, it can involve a one-dimensional model configuration scheme and a two-dimensional model configuration scheme.

[0102] The so-called one-dimensional model configuration scheme means that the model output is specifically configured as a one-dimensional data format. Similarly, the two-dimensional model configuration scheme corresponds to the model output in a two-dimensional data format.

[0103] Next, we will further explain these two types of configuration solutions.

[0104] As an exemplary implementation, in a one-dimensional model configuration scheme, the labeling results use 0 to represent a normal type, 1 to represent a cardiac inhibition type, 2 to represent a vascular inhibition type, and 3 to represent a mixed type.

[0105] Correspondingly, the syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers represent the probabilities of normal type, cardioinhibitory type, vasoinhibitory type and mixed type, respectively.

[0106] In this way, the prediction results of the syncope prediction model on whether syncope exists and the specific syncope type can be conveniently identified through a one-dimensional array of length 4. On this basis, a final syncope prediction result can be fused, and the maximum probability and its corresponding specific syncope type are taken as output.

[0107] In addition, to ensure processing accuracy and efficiency, this application also makes more specific restrictions on the input format of the syncope prediction model.

[0108] (1) The sample electrocardiogram data is specifically the electrocardiogram data measured by lead II.

[0109] It can be understood that the so-called electrocardiogram data may involve or include electrocardiogram data measured by different leads. Taking a 12-lead electrocardiograph as an example, the electrocardiogram data measured by the 12-lead electrocardiograph is 12-lead electrocardiogram data.

[0110] However, the present application believes that some leads have outstanding contributions to the goal of syncope prediction, while some leads do not have outstanding contributions to the goal of syncope prediction and may even cause interference.

[0111] In this regard, this application specifically focuses on the electrocardiogram data involved in the solution on lead II. The corresponding electrocardiograph only needs to be able to collect lead II electrocardiogram data, and there is no need to limit the lead format of the specific electrocardiograph.

[0112] In this way, in actual operation, the amount of data involved can be highly simplified, taking into account both processing efficiency and processing accuracy, while also helping to reduce the model training cost or the configuration cost of model training. In addition, in the subsequent model application link, it also helps to achieve lower application costs. For example, the electrocardiograph can only collect the electrocardiogram data of the second lead, and the other leads do not need to work, which greatly simplifies the workload of medical staff responsible for measuring electrocardiograms. At the same time, it can also greatly reduce the storage / transmission cost of the model input data, and the model can carry out more efficient and high-precision syncope prediction processing.

[0113] (2) Taking the training sample as an example, based on the labeled sample electrocardiogram data, before training the syncope prediction model, the method of the present application may further include:

[0114] Discard the first two minutes of the sample ECG data to obtain the remaining data;

[0115] Based on the sliding window method, the remaining data is cut into multiple segments according to the cutting unit of 7200 data length corresponding to ten minutes. In addition, for different types of sample ECG data, 6 segments of data are randomly retained for each normal user, all segments of data are retained for each user with cardiac inhibition, and 24 segments of data are randomly retained for each user with vascular inhibition and each mixed user, completing the update of the sample ECG data.

[0116] It can be understood that the setting of the embodiment here is a pre-processing means.

[0117] This application believes that in actual situations, the electrocardiogram data measured in the first two minutes may contain blanks or interference, so it is discarded / eliminated.

[0118] In addition, in order to simplify the input data and take into account both training quality and training efficiency, the ECG data can be cut / split / segmented / truncated using a sliding window method based on a cutting unit of 7200 data length corresponding to ten minutes to obtain several segments of high-quality ECG data.

[0119] In addition, in order to further enhance the training effect, considering that the sample sizes of each syncope type vary greatly in actual situations, and also considering the possibility of facing different syncope types in the actual prediction process of the balanced model, the data volume of each sample user, that is, the number of segments, is also limited based on the number of segments to balance the sample size differences between different syncope types.

[0120] (3) Under the above settings, the syncope prediction model can be configured to input data with a tensor shape of [n, 1, 72000] and a label corresponding to each sequence segment (the label is consistent with the label before segmentation), where n is the number of sequence segments.

[0121] In addition, under the one-dimensional model configuration scheme, this application also provides a practical configuration scheme from the perspective of specific model architecture.

[0122] For details, see Figure 2 A schematic diagram of a network architecture of a one-dimensional convolutional neural network of the present application is shown. As an exemplary embodiment, the syncope prediction model can be a one-dimensional variant network based on VGG-16. Correspondingly, the model architecture of the syncope prediction model has the following configuration content:

[0123] The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling;

[0124] The two convolutional layers connected before and after ( Figure 2 After that, a pooling layer ( Figure 2 Then continue to connect two front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then continue to connect two front-to-back fully connected layers configured with ReLU functions ( Figure 2 denoted as Fully connected+ReLU), followed by a fully connected layer with a Softmax function ( Figure 2It is shown as Fully connected+Softmax), with a total of 13 convolutional layers and 3 fully connected layers.

[0125] Among them, it is understandable that for the convolutional layers, pooling layers, ReLU functions and fully connected layers in different positions, the parameters will vary. In the above content, for the convenience of explanation, they are simply referred to as convolutional layers, pooling layers, ReLU functions and fully connected layers, that is, the types of specific model structures are pointed out without making further distinctions in the name details.

[0126] It can be understood that the embodiment here is a specific model architecture involved in the one-dimensional model configuration solution, and provides a set of specific implementation supporting solutions.

[0127] In addition, for the model, in specific applications, of course, other configuration work may also be involved.

[0128] For example, you can use the Adam optimizer to optimize, and set the initial learning rate to 0.0001 to adaptively adjust the learning rate. When verifying the performance of the model on the validation set, you can use accuracy, precision, recall, f1, and AUC as indicators for evaluating the model. These evaluation indicators are common indicators, so they are not described in detail here.

[0129] In addition, a five-fold cross-validation method can be used during model training to assist in evaluating the model training effect.

[0130] Next, we will focus on the two-dimensional model configuration scheme. As an exemplary implementation method, in the two-dimensional model configuration scheme, in the annotation results, the normal type is labeled as [0,0], the cardiac inhibition type is labeled as [1,0], the vascular inhibition type is labeled as [0,1], and the mixed type is labeled as [1,1].

[0131] It can be seen that in this embodiment, a multi-label method is adopted, and the labels are specifically in the form of a two-dimensional array.

[0132] In this case, the syncope prediction result of the syncope prediction model may include two predicted probability values ​​(also a two-dimensional array), and the output is 1 if the first probability value is greater than 0.5, and the output is 1 if the second predicted value is greater than 0.5.

[0133] In this way, the two predicted values ​​are combined to obtain the final prediction result.

[0134] At the same time, for the two-dimensional model configuration scheme, this application also provides a practical configuration scheme from the perspective of specific model architecture.

[0135] For details, seeFigure 3 A schematic diagram of a network architecture of a two-dimensional convolutional neural network of the present application is shown. As an exemplary embodiment, the syncope prediction model can be a two-dimensional variant network based on VGG-16. Correspondingly, the model architecture of the syncope prediction model has the following configuration content:

[0136] The number of convolution kernels connected before and after is 64. Figure 3 After that, a pooling layer ( Figure 3 Then continue to connect two convolutional layers with 128 convolution kernels, then connect a pooling layer, then continue to connect three convolutional layers with 256 convolution kernels, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels, then continue to connect three fully connected layers with ReLU functions ( Figure 3 FC), followed by a Sigmoid function ( Figure 3 The convolutional layers are all configured with ReLU functions ( Figure 3 (denoted as ReLU).

[0137] Among them, it is understandable that for the convolutional layers, pooling layers, ReLU functions and fully connected layers in different positions, the parameters will vary. In the above content, for the convenience of explanation, they are simply referred to as convolutional layers, pooling layers, ReLU functions and fully connected layers, that is, the types of specific model structures are pointed out without making further distinctions in the name details.

[0138] It can be understood that the embodiment here is a specific model architecture involved in the two-dimensional model configuration solution, and provides a set of specific implementation supporting solutions.

[0139] The above is an introduction to the configuration method of the syncope prediction model provided in this application. In order to facilitate better implementation of the configuration method of the syncope prediction model provided in this application, this application also provides a configuration device of the syncope prediction model from the perspective of functional modules.

[0140] See Figure 4 , Figure 4 FIG. 4 is a schematic diagram of a configuration device for a syncope prediction model of the present application. In the present application, the configuration device 400 for a syncope prediction model may specifically include the following structure:

[0141] An acquisition unit 401 is configured to acquire sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period;

[0142] a configuration unit 402 configured to configure corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope, and the annotation results are further used to indicate the syncope type if the user corresponding to the sample ECG data has experienced syncope, wherein the syncope types include cardioinhibitory, vasoinhibitory, and mixed syncope;

[0143] The training unit 403 is used to train a syncope prediction model based on the labeled sample electrocardiogram data, wherein the syncope prediction model is used to predict whether the user will experience syncope within a preset time period in the future based on the electrocardiogram data input into the model and the specific syncope type if syncope occurs.

[0144] In an exemplary embodiment, in a one-dimensional model configuration scheme, the labeling results are 0 for normal type, 1 for cardiac inhibition type, 2 for vascular inhibition type, and 3 for mixed type;

[0145] The syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers represent the probabilities of normal type, cardiac inhibition type, vasoinhibition type and mixed type respectively.

[0146] In another exemplary embodiment, the apparatus further includes a pre-processing unit 404, configured to:

[0147] Discard the first two minutes of the sample ECG data to obtain the remaining data;

[0148] Using a sliding window approach, the remaining data is divided into multiple segments, each with a length of 7200 data points, corresponding to ten minutes. Furthermore, for each type of sample ECG data, six segments are randomly retained for each normal user, all segments are retained for each cardioinhibited user, and 24 segments are randomly retained for each vasoinhibited user and each mixed user, completing the update of the sample ECG data.

[0149] The syncope prediction model is configured to take a tensor of shape [n, 1, 72000] and a label for each sequence segment, where n is the number of sequence segments.

[0150] In another exemplary embodiment, the syncope prediction model is specifically a one-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content:

[0151] The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling;

[0152] After the two front-to-back convolutional layers, connect a pooling layer, then continue to connect two front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then connect a pooling layer, then continue to connect three front-to-back convolutional layers, then continue to connect two front-to-back fully connected layers configured with ReLU functions, and then continue with a fully connected layer configured with Softmax function.

[0153] In another exemplary embodiment, in a two-dimensional model configuration scheme, in the annotation results, the label assigned to the normal type is [0,0], the label assigned to the cardiac inhibition type is [1,0], the label assigned to the vascular inhibition type is [0,1], and the label assigned to the mixed type is [1,1];

[0154] The syncope prediction result of the syncope prediction model includes two predicted probability values. If the first probability value is greater than 0.5, the output is 1, and if the second predicted value is greater than 0.5, the output is 1.

[0155] In another exemplary embodiment, the syncope prediction model is specifically a two-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content:

[0156] After the two convolutional layers with 64 convolution kernels connected front and back, connect a pooling layer, then continue to connect two convolutional layers with 128 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 256 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then connect a pooling layer, then continue to connect three convolutional layers with 512 convolution kernels connected front and back, then continue to connect three fully connected layers with ReLU functions connected front and back, then continue with a Sigmoid function, and all convolutional layers are configured with ReLU functions.

[0157] In another exemplary embodiment, the apparatus further includes an application unit 405 configured to:

[0158] Get the current user's electrocardiogram data;

[0159] Inputting the current user's electrocardiogram data into the syncope prediction model to perform corresponding syncope prediction processing;

[0160] extracting the syncope prediction processing result output by the syncope prediction model;

[0161] Based on the syncope prediction processing result, a syncope intervention measure reminder is output to the current user.

[0162] This application also provides a processing device from the perspective of hardware structure, see Figure 5 , Figure 5 The schematic diagram of the structure of the processing device of the present application is shown. Specifically, the processing device of the present application may include a processor 501, a memory 502 and an input / output device 503. The processor 501 is used to execute the computer program stored in the memory 502 to implement the following Figure 1 The steps of the configuration method of the syncope prediction model in the corresponding embodiment; or, the processor 501 is used to execute the computer program stored in the memory 502 to implement the following Figure 4 The memory 502 is used to store the functions of each unit in the embodiment, and the processor 501 executes the above Figure 1 A computer program required for the configuration method of the syncope prediction model in the corresponding embodiment.

[0163] For example, the computer program may be divided into one or more modules / units, one or more of which are stored in the memory 502 and executed by the processor 501 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in a computer device.

[0164] The processing device may include, but is not limited to, a processor 501, a memory 502, and an input / output device 503. Those skilled in the art will appreciate that the illustrations are merely examples of processing devices and do not limit the processing device. The processing device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 501, the memory 502, the input / output device 503, etc. are connected via the bus.

[0165] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the processing device and connects various parts of the entire device using various interfaces and lines.

[0166] The memory 502 can be used to store computer programs and / or modules. The processor 501 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 502 and accessing the data stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store an operating system, at least one application required for a function, etc.; the data storage area may store data created based on the use of the processing device, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0167] When the processor 501 is used to execute the computer program stored in the memory 502, it can specifically implement the following functions:

[0168] Acquiring sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period;

[0169] Configuring corresponding annotation results for the sample ECG data, wherein the annotation results are used to indicate whether the user corresponding to the sample ECG data has experienced syncope. The annotation results are also used to indicate the syncope type if the user corresponding to the sample ECG data has experienced syncope. The syncope types include cardioinhibitory, vasoinhibitory, and mixed syncope types.

[0170] Based on the labeled sample ECG data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user will experience syncope within a preset time period in the future based on the ECG data input to the model, as well as the specific syncope type if syncope occurs.

[0171] Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working process of the configuration device, processing equipment and corresponding units of the syncope prediction model described above can refer to the following. Figure 1 The description of the configuration method of the syncope prediction model in the corresponding embodiment will not be repeated here.

[0172] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0173] To this end, the present application provides a computer-readable storage medium, which stores a plurality of instructions, which can be loaded by a processor to execute the present application as follows: Figure 1 The steps of the configuration method of the syncope prediction model in the corresponding embodiment, the specific operations can be referred to as follows Figure 1 The description of the configuration method of the syncope prediction model in the corresponding embodiment will not be repeated here.

[0174] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0175] Due to the instructions stored in the computer readable storage medium, the present application can be executed as follows: Figure 1 The steps of the configuration method of the syncope prediction model in the corresponding embodiment, therefore, the present application can be realized as follows Figure 1 The beneficial effects that can be achieved by the configuration method of the syncope prediction model in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0176] The above is a detailed introduction to the configuration method, device, processing equipment and computer-readable storage medium of the syncope prediction model provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for configuring a syncope prediction model, characterized in that: The method comprises: Acquiring sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period; Configuring corresponding annotation results for the sample electrocardiogram data, wherein the annotation results are used to indicate whether the user corresponding to the sample electrocardiogram data has experienced syncope, and the annotation results are further used to indicate the syncope type if the user corresponding to the sample electrocardiogram data has experienced syncope, wherein the syncope types include cardioinhibitory type, vasoinhibitory type, and mixed type; Based on the labeled sample electrocardiogram data, a syncope prediction model is trained, wherein the syncope prediction model is used to predict whether the user corresponding to the electrocardiogram data input to the model will experience syncope within a preset time period in the future and the specific syncope type if syncope occurs; In the one-dimensional model configuration scheme, the labeling result is represented by 0 for the normal type, 1 for the cardiac inhibition type, 2 for the vascular inhibition type, and 3 for the mixed type; The syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers respectively represent the probabilities of the normal type, the cardioinhibitory type, the vasoinhibitory type, and the mixed type; The sample electrocardiogram data is specifically the electrocardiogram data measured by lead II. Before training the syncope prediction model based on the labeled sample electrocardiogram data, the method further includes: discarding the first two minutes of the sample electrocardiogram data to obtain the remaining data; Based on a sliding window approach, the remaining data is cut into multiple segments of data, each corresponding to a data length of 7200 data points for ten minutes. Furthermore, for different types of sample ECG data, six segments of data are randomly retained for each normal user, all segments are retained for each cardioinhibitory user, and 24 segments of data are randomly retained for each vasoinhibitory user and each mixed user, thereby completing the update of the sample ECG data. The syncope prediction model is specifically configured to input a tensor of shape [n, 1, 72000] and a label corresponding to each sequence segment for the input data of the model, where n is the number of sequence segments; The syncope prediction model is specifically a one-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content: The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling; After the two convolutional layers connected front and back, a pooling layer is connected, followed by two convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by two fully connected layers connected front and back with ReLU functions, followed by one fully connected layer with Softmax functions. Alternatively, in a two-dimensional model configuration scheme, in the annotation result, the label assigned to the normal type is [0, 0], the label assigned to the cardiac inhibition type is [1, 0], the label assigned to the vascular inhibition type is [0, 1], and the label assigned to the mixed type is [1, 1]; The syncope prediction result of the syncope prediction model includes two predicted probability values, the first probability value is greater than 0.5 and the output is 1, and the second probability value is greater than 0.5 and the output is 1; The syncope prediction model is specifically a two-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content: After the two convolutional layers with 64 convolution kernels connected front and back, a pooling layer is connected, and then the two convolutional layers with 128 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 256 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 512 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 512 convolution kernels connected front and back are connected, and then the three fully connected layers configured with the ReLU function are connected front and back, and then a Sigmoid function is connected, and the convolutional layers are all configured with the ReLU function.

2. The method according to claim 1, characterized in that The method further comprises: Get the current user's electrocardiogram data; Inputting the electrocardiogram data of the current user into the syncope prediction model to perform corresponding syncope prediction processing; extracting a syncope prediction processing result output by the syncope prediction model; Based on the syncope prediction processing result, a syncope intervention measure reminder is output to the current user.

3. A device for configuring a syncope prediction model, characterized in that: The device comprises: an acquiring unit, configured to acquire sample electrocardiogram data, wherein the sample electrocardiogram data is specifically long-term electrocardiogram data within a normal time period; a configuration unit, configured to configure corresponding annotation results for the sample electrocardiogram data, wherein the annotation results are used to indicate whether the user corresponding to the sample electrocardiogram data has experienced syncope, and the annotation results are further used to indicate the syncope type if the user corresponding to the sample electrocardiogram data has experienced syncope, wherein the syncope types include cardiac inhibition type, vasoinhibition type, and mixed type; a training unit for training a syncope prediction model based on the labeled sample electrocardiogram data, wherein the syncope prediction model is used to predict, based on the electrocardiogram data input to the model, whether the user corresponding to the electrocardiogram data input to the model will experience syncope within a preset time period in the future and the specific syncope type if syncope occurs; In the one-dimensional model configuration scheme, the labeling result is represented by 0 for the normal type, 1 for the cardiac inhibition type, 2 for the vascular inhibition type, and 3 for the mixed type; The syncope prediction result of the syncope prediction model is a one-dimensional array of length 4, and the four output numbers respectively represent the probabilities of the normal type, the cardioinhibitory type, the vasoinhibitory type, and the mixed type; The sample electrocardiogram data is specifically the electrocardiogram data measured by lead II. The device further includes a preprocessing unit for: discarding the first two minutes of the sample electrocardiogram data to obtain the remaining data; Based on a sliding window approach, the remaining data is cut into multiple segments of data, each corresponding to a data length of 7200 data points for ten minutes. Furthermore, for different types of sample ECG data, six segments of data are randomly retained for each normal user, all segments are retained for each cardioinhibitory user, and 24 segments of data are randomly retained for each vasoinhibitory user and each mixed user, thereby completing the update of the sample ECG data. The syncope prediction model is specifically configured to input a tensor of shape [n, 1, 72000] and a label corresponding to each sequence segment for the input data of the model, where n is the number of sequence segments; The syncope prediction model is specifically a one-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content: The number of convolution kernels in the first convolution layer is set to 16, and the number of convolution kernels is doubled after each pooling; After the two convolutional layers connected front and back, a pooling layer is connected, followed by two convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by one pooling layer, followed by three convolutional layers connected front and back, followed by two fully connected layers configured with ReLU functions, followed by one fully connected layer configured with Softmax functions; Alternatively, in a two-dimensional model configuration scheme, in the annotation result, the label assigned to the normal type is [0, 0], the label assigned to the cardiac inhibition type is [1, 0], the label assigned to the vascular inhibition type is [0, 1], and the label assigned to the mixed type is [1, 1]; The syncope prediction result of the syncope prediction model includes two predicted probability values, the first probability value is greater than 0.5 and the output is 1, and the second probability value is greater than 0.5 and the output is 1; The syncope prediction model is specifically a two-dimensional variant network based on VGG-16. The model architecture of the syncope prediction model has the following configuration content: After the two convolutional layers with 64 convolution kernels connected front and back, a pooling layer is connected, and then the two convolutional layers with 128 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 256 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 512 convolution kernels connected front and back are connected, and then the pooling layer is connected, and then the three convolutional layers with 512 convolution kernels connected front and back are connected, and then the three fully connected layers configured with the ReLU function are connected front and back, and then a Sigmoid function is connected, and the convolutional layers are all configured with the ReLU function.

4. A processing device, characterized in that The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method according to claim 1 or 2 is executed.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to claim 1 or 2.

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