CN112885459A

CN112885459A - System and device for predicting ventricular hypertrophy and storage medium

Info

Publication number: CN112885459A
Application number: CN202110175595.4A
Authority: CN
Inventors: 黄桂芳; 吴�琳; 刘华清; 钱孝贤; 韩蓝青; 余显冠; 凌叶盛
Original assignee: Research Institute Of Tsinghua Pearl River Delta; Third Affiliated Hospital Sun Yat Sen University
Current assignee: Research Institute Of Tsinghua Pearl River Delta; Third Affiliated Hospital Sun Yat Sen University
Priority date: 2021-02-09
Filing date: 2021-02-09
Publication date: 2021-06-01

Abstract

The application discloses a ventricular hypertrophy prediction system, a ventricular hypertrophy prediction device and a storage medium. The system comprises: the acquisition module is used for acquiring electrocardiogram data of a person to be predicted in multiple lead modes; the grouping module is used for grouping the lead modes and dividing the electrocardiogram data according to the grouping result of the lead modes to obtain a plurality of electrocardiogram data groups; the prediction module is used for inputting each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy or not; and the processing module is used for obtaining a ventricular hypertrophy prediction result of the person to be predicted according to the weighted result of each first prediction result. The method and the device can effectively overcome the condition that the accuracy of the prediction result is low due to a single data source and a single prediction model. The method and the device can be widely applied to the technical field of artificial intelligence.

Description

System and device for predicting ventricular hypertrophy and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a system and a device for predicting ventricular hypertrophy and a storage medium.

Background

Ventricular hypertrophy (ventricular hypertrophy) is a cardiological disease mainly caused by overloading of the ventricles (either in diastole or in systole), including ventricular hypertrophy and enlargement, wherein the overstressing of the pressure is mainly ventricular hypertrophy, and the overloading of the volume is mainly ventricular enlargement: after a long load time, the film tends to be both thickened and enlarged. Ventricular hypertrophy is a common consequence of organic heart disease and can be manifested on electrocardiograms when reaching a certain degree.

In the diagnosis of ventricular hypertrophy, the electrocardiographic data of some patients may be indistinguishable from the electrocardiographic data in a normal state, and the person artificially judges whether ventricular hypertrophy exists in the patient according to the electrocardiographic data, so that a great deal of clinical experience is required, and the obtained prediction result may be not as expected. In the related art, there are cases where a machine-learned model is used to predict whether a patient has a disease, but the reliability of the prediction result obtained by such a model is not high, and misdiagnosis is likely to occur. In view of the above, there is a need to solve the technical problems in the related art.

Disclosure of Invention

The present application aims to solve at least one of the technical problems in the related art to some extent.

To this end, it is an object of the embodiments of the present application to provide a ventricular hypertrophy prediction system which can effectively improve the accuracy of predicting whether a user has ventricular hypertrophy.

In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the application comprises the following steps:

in a first aspect, an embodiment of the present application provides a system for predicting ventricular hypertrophy, including:

the acquisition module is used for acquiring electrocardiogram data of a person to be predicted in multiple lead modes;

the grouping module is used for grouping the lead modes and dividing the electrocardiogram data according to the grouping result of the lead modes to obtain a plurality of electrocardiogram data groups;

the prediction module is used for inputting each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy or not;

and the processing module is used for obtaining a ventricular hypertrophy prediction result of the person to be predicted according to the weighted result of each first prediction result.

In addition, the system according to the above embodiment of the present application may further have the following additional technical features:

further, in one embodiment of the present application, the grouping module is configured to randomly group the lead patterns.

Further, in an embodiment of the present application, the randomly grouping the lead patterns specifically includes:

randomly grouping the lead modes to obtain a plurality of lead sets; wherein the number of lead patterns in each of the lead sets is the same.

Further, in an embodiment of the present application, the system further includes an equalization processing module;

the equalization processing module is used for carrying out equalization processing on the electrocardiogram data through random copy.

Further, in one embodiment of the present application, the system further comprises a denoising processing module;

the denoising processing module is used for denoising the electrocardiogram data through a wavelet transform technology.

Further, in an embodiment of the present application, the processing module is specifically configured to:

determining the weight corresponding to each first prediction result according to the electrocardiogram data amount of each electrocardiogram data set;

determining a weighted result of each first prediction result according to the weight;

and predicting the result of the ventricular hypertrophy of the person to be predicted according to the weighting result.

Further, in an embodiment of the present application, the prediction module is specifically configured to:

inputting the electrocardiogram data set into a convolutional neural network for feature extraction to obtain a feature vector of the electrocardiogram data set;

and inputting the characteristic vectors into a long-term and short-term memory network for classification prediction to obtain the first prediction result corresponding to the electrocardiogram data set.

In a second aspect, an embodiment of the present application provides a ventricular hypertrophy prediction apparatus, comprising:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, cause the at least one processor to perform the steps of:

acquiring electrocardiogram data of a person to be predicted in multiple lead modes;

grouping the lead modes, and dividing the electrocardiogram data according to the grouping result of the lead modes to obtain a plurality of electrocardiogram data sets;

inputting each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy or not;

and obtaining a ventricular hypertrophy prediction result of the person to be predicted according to the weighted result of each first prediction result.

In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, the method includes:

Advantages and benefits of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application:

the prediction system in the embodiment of the application acquires electrocardiogram data in a plurality of lead modes when ventricular hypertrophy is predicted; randomly grouping the lead modes, and dividing the electrocardiogram data according to the grouping result of the lead modes to obtain a plurality of electrocardiogram data groups; inputting each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; and obtaining a ventricular hypertrophy prediction result according to the weighted result of the first prediction results. In the ventricular hypertrophy prediction process, the system synthesizes multi-lead electrocardiogram data on the data source, synthesizes prediction results of multiple models in prediction, and uses the electrocardiogram data in different lead modes in different prediction models, thereby effectively overcoming the defect of low precision of the prediction results caused by a single data source and a single prediction model.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made on the drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for predicting ventricular hypertrophy in accordance with the present application;

FIG. 2 is a schematic diagram of an embodiment of a system for predicting ventricular hypertrophy in accordance with the present invention;

fig. 3 is a schematic structural diagram of an embodiment of a device for predicting ventricular hypertrophy according to the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

In the embodiment of the application, in order to effectively improve the prediction accuracy of whether a person to be predicted (generally, a patient in a hospital) has a ventricular hypertrophy disease, electrocardiogram data under multiple leads are grouped according to a lead mode, then the grouped electrocardiogram data are input into a plurality of prediction models to obtain first prediction results of the electrocardiogram data under each group, and then comprehensive judgment is carried out according to the prediction results, so that a finally required ventricular hypertrophy prediction result is obtained. According to the technical scheme, multi-lead electrocardiogram data are integrated on a data source, prediction results of multiple models are integrated on prediction, the electrocardiogram data in different lead modes are used in different prediction models, and the defect that the accuracy of the prediction results is low due to a single data source and a single prediction model is overcome effectively.

Referring to fig. 1, the present embodiment provides a ventricular hypertrophy prediction method, which may be applied in a terminal, a server, software running in the terminal or the server, or the like. The terminal may be, but is not limited to, a tablet computer, a notebook computer, a desktop computer, and the like. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and a big data and artificial intelligence platform. The method mainly comprises the following steps:

step 110, acquiring electrocardiogram data of a person to be predicted in a plurality of lead modes;

in the embodiment of the application, for a person to be predicted who wants to predict whether ventricular hypertrophy diseases exist, electrocardiogram data can be obtained firstly, specifically, the actual source of the electrocardiogram data is that electrodes are placed at different parts of a human body and are connected with the positive electrode and the negative electrode of an electrocardiograph current meter through lead wires, so that the electrocardiogram data are obtained through collection, and different leads can be formed due to different electrode positions and different connection methods. The leads are placed at different positions, and the obtained detection data have some differences, and different leads can be regarded as analyzing the state of the heart from different directions. In the related art, generally widely adopted is an international common lead system, called conventional 12-lead, including a limb lead connected to a limb and a chest lead connected to a chest. In particular, limb leads include standard limb lead 1, standard limb lead 2, standard limb lead 3, and pressurized monopolar limb lead aVR, pressurized monopolar limb lead aVL, pressurized monopolar limb lead aVF. Chest leads belong to unipolar leads, including the V1-V6 leads. In the embodiment of the present application, the obtained electrocardiographic data may be in the 12-lead manner, that is, there is electrocardiographic detection data corresponding to each lead manner. It should be understood that the electrocardiogram data acquired in the embodiment of the present application may be directly detected or received through a data transmission channel, and the number of leads involved in the electrocardiogram data may be any number greater than or equal to 2.

Optionally, in the embodiment of the present application, when obtaining the electrocardiographic data, data of 5 heart cycles (60 times/minute, one heart cycle 1s, that is, 1000 sampling points, and 5 cycles total 5000 sampling points) may be intercepted as one electrocardiographic data in one lead mode. Moreover, for each electrocardiogram data, wavelet transformation can be adopted to denoise the electrocardiogram data, namely a wavelet transformation threshold denoising method. The method is a nonlinear denoising method, can achieve approximate optimization in the meaning of minimum mean square error, and has the characteristics of simplest realization and minimum calculated amount. The basic principle is as follows: orthogonal wavelet decomposition has the capacity of time-frequency local decomposition, and when signal processing is carried out, the amplitude of wavelet component expression is large, which is just obviously contrasted with the uniform change of noise in a high-frequency part. After wavelet decomposition, most of wavelet coefficients with larger amplitude are useful signals, while coefficients with smaller amplitude are generally noise, i.e. the wavelet transform coefficients of the useful signals are considered to be larger than those of the noise. The threshold denoising method is to find a proper threshold, retain the wavelet coefficient larger than the threshold, correspondingly process the wavelet coefficient smaller than the threshold, and then restore the useful signal according to the processed wavelet coefficient. It can be understood that, in the embodiment of the present application, when the wavelet transform denoising is adopted, the set threshold may be flexibly adjusted according to needs. Optionally, in the embodiment of the present application, the electrocardiographic data may be randomly copied to enrich and balance the data samples, so that a plurality of subsequent different prediction models have sufficient data size to perform prediction.

Step 120, grouping the lead modes, and dividing the electrocardiogram data according to the grouping results of the lead modes to obtain a plurality of electrocardiogram data sets;

in this step, the lead patterns are first randomly grouped, and optionally, all the lead patterns may be equally divided into several groups, taking the standard 12-lead pattern as an example, the 12 lead patterns may be randomly divided into 4 groups, each group being denoted as a lead set, for example, the first lead set includes four lead patterns of a standard limb lead 1, a standard limb lead 2, a standard limb lead 3 and a pressurized unipolar limb lead aVR, the second lead set includes four lead patterns of a pressurized unipolar limb lead aVL, a pressurized unipolar limb lead aVF, a chest lead V1 and a chest lead V2, and the third lead set includes four lead patterns of a chest lead V3, a chest lead V4, a chest lead V5 and a chest lead V6. Then, according to the grouping result of the lead modes, dividing the electrocardiogram data to obtain a plurality of electrocardiogram data sets, namely dividing the electrocardiogram data obtained by the lead modes in the first lead set into a group, and recording the group as a first electrocardiogram data set; dividing electrocardiogram data obtained in a lead mode under the second lead set into a group, and recording the group as a second electrocardiogram data group; the electrocardiogram data obtained by the lead mode in the third lead set is divided into a group and is marked as a third electrocardiogram data group. It should be noted that, in the embodiment of the present application, both the number of groups and the manner of grouping the lead modes may be flexibly adjusted according to the needs, and the number of the lead modes in each lead set is not fixed to the above number.

Step 130, inputting each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy or not;

in this step, each electrocardiogram data set is input to a different prediction model, and a first prediction result output by each prediction model for the corresponding electrocardiogram data set is obtained. Still taking the first, second and third electrocardiogram data sets as an example, the first electrocardiogram data set is input into a prediction model to obtain a first prediction result, and similarly, the second electrocardiogram data set and the third electrocardiogram data set are respectively input into other prediction models to obtain a corresponding first prediction result. The first prediction result is used to indicate whether the person to be predicted has ventricular hypertrophy, for example, the first prediction result may be a numerical value, which indicates that the person to be predicted does not have ventricular hypertrophy when the first prediction result is 0, and indicates that the person to be predicted has ventricular hypertrophy when the first prediction result is 1.

In the embodiment of the application, three groups of data are processed by different prediction models, so that the comprehensive reliability of the prediction result can be improved, and the prediction error possibly caused by one prediction model can be reduced. It should be understood that the different prediction models in the embodiments of the present application may be prediction models with different structures and/or parameters, or prediction models trained by using different data sets. Specifically, the prediction model in the embodiment of the present application may be a network structure formed by combining a convolutional neural network and a long-short term memory network, for example, the feature of the electrocardiogram data set may be extracted through the convolutional neural network to obtain a feature vector of the electrocardiogram data set, and then the feature vector is input to the long-short term memory network for classification prediction, so as to obtain a corresponding first prediction result.

And 140, obtaining a ventricular hypertrophy prediction result according to the weighted result of the first prediction results.

In the embodiment of the present application, the final ventricular hypertrophy prediction result is determined based on the first prediction results of the respective electrocardiographic data sets. Assuming that the first prediction results output by the prediction model represent the probability of ventricular hypertrophy of the person to be predicted, in some embodiments, the average of the first prediction results may be directly obtained, and the average probability is used as the final probability of ventricular hypertrophy of the person to be predicted. Then comparing the final probability with a preset probability threshold, and outputting a ventricular hypertrophy prediction result that the person to be predicted suffers from ventricular hypertrophy when the final probability is higher than the probability threshold; and conversely, when the final probability is lower than the probability threshold, the output ventricular hypertrophy prediction result is that the person to be predicted does not suffer from ventricular hypertrophy.

Optionally, in this embodiment of the present application, step 140 may specifically include:

1401, determining weights corresponding to the first prediction results according to electrocardiogram data quantities of the electrocardiogram data sets;

step 1402, determining a weighted result of each first prediction result according to the weight;

and 1403, predicting the ventricular hypertrophy of the person to be predicted according to the weighting result.

In the embodiment of the present application, the weighting weight corresponding to each first prediction result may be adjusted according to the amount of electrocardiographic data of the electrocardiographic data group corresponding to each first prediction result, for example, when the amount of electrocardiographic data of the electrocardiographic data group corresponding to the first prediction result is large, the weighting weight of the first prediction result may be appropriately increased, and when the amount of electrocardiographic data of the electrocardiographic data group corresponding to the first prediction result is small, the weighting of the first prediction result may be appropriately decreased, thereby reducing prediction errors caused by imbalance of the amount of data and improving the prediction accuracy of the obtained ventricular hypertrophy prediction result.

A ventricular hypertrophy prediction system proposed according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

Referring to fig. 2, a ventricular hypertrophy prediction system provided in an embodiment of the present application includes:

the acquisition module 101 is used for acquiring electrocardiogram data of a person to be predicted in multiple lead modes;

a grouping module 102, configured to group the lead manners, and divide the electrocardiogram data according to the grouping results of the lead manners to obtain a plurality of electrocardiogram data sets;

the prediction module 103 is configured to input each electrocardiogram data set into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data set; the first prediction result is used for representing whether the person to be predicted has ventricular hypertrophy or not;

and the processing module 104 is configured to obtain a ventricular hypertrophy prediction result of the person to be predicted according to the weighted result of each first prediction result.

Optionally, in an embodiment of the present application, the grouping module 102 is configured to randomly group the lead patterns.

Optionally, in an embodiment of the present application, the randomly grouping the lead manners includes:

Optionally, in an embodiment of the present application, the system further includes an equalization processing module;

Optionally, in an embodiment of the present application, the system further includes a denoising processing module;

Optionally, in an embodiment of the present application, the processing module 104 is specifically configured to:

Optionally, in an embodiment of the present application, the prediction module 103 is specifically configured to:

It is to be understood that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.

Referring to fig. 3, an embodiment of the present application provides a ventricular hypertrophy prediction apparatus comprising:

at least one processor 201;

at least one memory 202 for storing at least one program;

means implemented by the at least one processor 201 when the at least one program is executed by the at least one processor 201.

Similarly, the contents of the method embodiments are all applicable to the apparatus embodiments, the functions specifically implemented by the apparatus embodiments are the same as the method embodiments, and the beneficial effects achieved by the apparatus embodiments are also the same as the beneficial effects achieved by the method embodiments.

The embodiment of the present application also provides a computer-readable storage medium, in which a program executable by the processor 201 is stored, and the program executable by the processor 201 is used for executing the above-mentioned apparatus when being executed by the processor 201.

Similarly, the contents in the above method embodiments are all applicable to the computer-readable storage medium embodiments, the functions specifically implemented by the computer-readable storage medium embodiments are the same as those in the above method embodiments, and the beneficial effects achieved by the computer-readable storage medium embodiments are also the same as those achieved by the above method embodiments.

In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.

Furthermore, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion regarding the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the present application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the application, which is defined by the appended claims and their full scope of equivalents.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: numerous changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.

While the present application has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A prediction system for ventricular hypertrophy, comprising:

The acquisition module is used to acquire the ECG data of the person to be predicted in various lead modes;

a grouping module, configured to group the lead modes, and divide the electrocardiogram data according to the grouping results of the lead modes to obtain a plurality of electrocardiogram data groups;

a prediction module, configured to input each electrocardiogram data group into different prediction models to obtain a first prediction result corresponding to each electrocardiogram data group; the first prediction result is used to characterize whether the person to be predicted has ventricular hypertrophy;

The processing module is configured to obtain the ventricular hypertrophy prediction result of the person to be predicted according to the weighted result of each of the first prediction results.

2 . The system according to claim 1 , wherein the grouping module is configured to randomly group the lead modes. 3 .

3. The system according to claim 2, wherein the random grouping of the lead modes is specifically:

The lead modes are randomly grouped to obtain a plurality of lead sets; wherein, the number of the lead modes in each of the lead sets is the same.

4. The system according to claim 1, characterized in that: the system further comprises an equalization processing module;

The equalization processing module is configured to perform equalization processing on the electrocardiogram data through random replication.

5. The system according to claim 1, characterized in that: the system further comprises a denoising processing module;

The denoising processing module is configured to perform denoising processing on the electrocardiogram data through wavelet transform technology.

6. The system according to any one of claims 1-5, wherein the processing module is specifically used for:

Determine the weight corresponding to each of the first prediction results according to the amount of ECG data in each of the ECG data groups;

According to the weight, determine the weighted result of each of the first prediction results;

According to the weighted result, the ventricular hypertrophy prediction result of the person to be predicted is obtained.

7. The system according to claim 1, wherein the prediction module is specifically used for:

Inputting the electrocardiogram data group into a convolutional neural network for feature extraction to obtain a feature vector of the electrocardiogram data group;

The feature vector is input into a long short-term memory network for classification prediction, and the first prediction result corresponding to the electrocardiogram data set is obtained.

8. A device for predicting ventricular hypertrophy, comprising:

at least one processor;

at least one memory for storing at least one program;

When the at least one program is executed by the at least one processor, the at least one processor implements the following steps:

Obtain the ECG data of the person to be predicted in various lead modes;

Grouping the lead modes, and dividing the electrocardiogram data according to the grouping results of the lead modes, to obtain a plurality of electrocardiogram data groups;

Input each electrocardiogram data group into different prediction models, and obtain the first prediction result corresponding to each electrocardiogram data group; the first prediction result is used to characterize whether the person to be predicted has ventricular hypertrophy;

According to the weighted result of each of the first prediction results, the ventricular hypertrophy prediction result of the person to be predicted is obtained.

9. A computer-readable storage medium, wherein a program executable by a processor is stored, wherein the program executable by the processor is used to implement the following steps when executed by the processor:

Obtain the ECG data of the person to be predicted in various lead modes;