A method and apparatus for providing information necessary for the classification and diagnosis of arrhythmias using 12-lead electrocardiogram signals.

Abstract

Disclosed are a method for providing information required for arrhythmia classification and diagnosis using a 12-lead ECG signal and a device for performing the same.SOLUTION: A method of providing information required for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention is a method of providing information required for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals performed in a computing device including one or more processors and a memory storing one or more programs executed by the one or more processors, the method including acquiring 12-lead electrocardiogram signals, outputting an analysis result of the 12-lead electrocardiogram signals using a MachineLearning-based technique, and generating information required for arrhythmia classification and diagnosis based on the analysis result.SELECTED DRAWING: Figure 1

Description

Technical Field

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[0001] Embodiments of the present invention relate to a technique for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals.

Background Art

[0002] According to the World Health Organization (WHO), millions of people worldwide die annually due to heart disease. Electrocardiogram examination, known as a method for examining the presence or absence of heart disease, is a non-invasive and most well-known examination method that attaches electrodes to the skin and senses and records the electrical activity state that appears in the heart during the cardiac cycle. Generally, the presence or absence of heart disease such as arrhythmia is determined by acquiring and analyzing 12 electrocardiogram signals using 10 electrodes attached to the arms, legs, and chest.

[0003] Conventionally, doctors have determined heart disease through the recording of electrocardiogram signals recorded for each patient, and recently, attempts to use deep learning for the analysis and diagnosis of electrocardiogram signals have increased.

[0004] Conventional electrocardiogram signal analysis and diagnosis deep learning techniques extracted time-frequency features from single-lead electrocardiogram signals to classify arrhythmias. However, some arrhythmias are only observed in specific electrocardiogram channels, so the classification accuracy may decrease depending on the type of arrhythmia to be classified.

[0005] Therefore, for accurate diagnosis of arrhythmia, it is necessary to comprehensively and carefully check the electrocardiogram signals obtained from 12 channels. That is, since the electrocardiogram signals measured through 12 leads differ depending on the type of arrhythmia, it is necessary to comprehensively analyze the electrocardiogram signals obtained from 12 channels in order to accurately determine the patient's arrhythmia.

Prior Art Documents

Patent Documents

[0006] Korean Registered Patent Publication No. 10-2163217 (September 29, 2020) [Overview of the project] [Problems that the invention aims to solve]

[0007] Embodiments of the present invention are for providing information necessary for arrhythmia classification and diagnosis from 12-lead electrocardiogram signals using machine learning techniques. [Means for solving the problem]

[0008] According to an exemplary embodiment of the present invention, a method is provided for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal, performed on a computing device having one or more processors and memory for storing one or more programs executed by the one or more processors, the method comprising the steps of: acquiring a 12-lead electrocardiogram signal; outputting analysis results for the 12-lead electrocardiogram signal from the 12-lead electrocardiogram signal using machine learning-based techniques; and generating information necessary for arrhythmia classification and diagnosis based on the analysis results.

[0009] The step of outputting the classification result can receive the input of the 12-lead electrocardiogram signal through a pre-trained artificial neural network model and classify the type of arrhythmia based on the 12-lead electrocardiogram signal.

[0010] The classification step further includes: outputting an initial feature map through a convolution operation based on the input 12-lead electrocardiogram signal using an initial feature block; outputting a concentrated feature map through an element-wise weighting operation based on the output initial feature map using an attention block; outputting a deep feature map through a shortcut operation based on the output initial feature map using a residual block; combining the output concentrated feature map and the output deep feature map using a join block to output a final feature map; and estimating probabilities for already set classes based on the output final feature map using a classification block, classifying the class with the highest probability into a type of arrhythmia, wherein the information necessary for arrhythmia classification and diagnosis may include the classified type of arrhythmia, the estimated probability, and the input 12-lead electrocardiogram signal.

[0011] The step of outputting the concentrated feature map may further include: performing maximum pooling and average pooling in parallel on the initial feature map through a maximum pooling layer and an average pooling layer; combining the pooling results output through the maximum pooling layer and the average pooling layer through a first join layer to output a weighted feature map; and combining the initial feature map and the weighted feature map through a second join layer to output the concentrated feature map.

[0012] The residual block is configured to include N short residual blocks (where N is a natural number greater than or equal to 2) sequentially linked to reflect the features of the initial feature map, and the N short residual blocks can be input with a previous feature map (the feature map output from the N-1th short residual block), output a new feature map from the previous feature map through a convolutional layer, and output a combined version of the previous feature map and the new feature map through a third joining layer.

[0013] The first and third join layers can use element-wise sum, while the second join layer can use element-wise multiplication.

[0014] The step of classifying the type of arrhythmia may further include the steps of performing global maximum pooling and global average pooling in parallel on the final feature map through a global maximum pooling layer and a global average pooling layer, and concatenating the pooling results output through the global maximum pooling layer and the global average pooling layer through a concatenation layer.

[0015] According to an exemplary embodiment of the present invention, a computing device is provided comprising one or more processors and a memory for storing one or more programs executed by the one or more processors, the computing device including a signal acquisition module for acquiring 12-lead electrocardiogram signals, a signal analysis module for outputting analysis results of the 12-lead electrocardiogram signals using machine learning-based technology, and a result provision module for generating information necessary for arrhythmia classification and diagnosis based on the analysis results. [Effects of the Invention]

[0016] According to embodiments of the present invention, by using machine learning technology to provide smart devices with information necessary for arrhythmia classification and diagnosis from standard 12-lead electrocardiogram signals, the time required for medical professionals (cardiology specialists) to determine arrhythmias can be reduced. [Brief explanation of the drawing]

[0017] [Figure 1] This is a block diagram illustrating the configuration of a device that provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to one embodiment of the present invention.

[0018] [Figure 2] It is a block diagram for explaining the configuration of a signal analysis module that constitutes an apparatus for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention.

[0019] [Figure 3] It is a diagram schematically showing the structure of a signal analysis module that constitutes an apparatus for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention.

[0020] [Figure 4] It is a diagram showing a screen of a smart device on which an analysis result provided by a result providing module of an apparatus for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention is displayed.

[0021] [Figure 5] It is a diagram for explaining a caution block of an apparatus for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention.

[0022] [Figure 6] It is a diagram for explaining a residual block of an apparatus for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention.

[0023] [Figure 7] It is a flowchart for explaining a method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to an embodiment of the present invention.

[0024] [Figure 8] It is a block diagram for exemplarily explaining a computing environment including a computing device suitable for use in an exemplary embodiment.

BEST MODE FOR CARRYING OUT THE INVENTION

[0025] Specific embodiments of the present invention will be described below with reference to the drawings. The following detailed description is provided to aid in a comprehensive understanding of the methods, apparatus and / or systems described herein. However, this is illustrative and the present invention is not limited thereto.

[0026] In describing embodiments of the present invention, if a specific description of known technology related to the present invention is deemed to unnecessarily obscure the gist of the invention, such detailed description will be omitted. Furthermore, terms used hereafter are defined in consideration of the function of the present invention, and these may vary depending on the intent or convention of the user or operator. Therefore, their definitions should be based on the overall content of this specification. Terms used in the detailed description are solely for describing embodiments of the present invention and should not be restrictive. Unless explicitly used otherwise, singular expressions imply the plural meaning. In this description, expressions such as “implication” or “presence” are intended to indicate a certain characteristic, number, step, action, element, part or combination thereof, and should not be interpreted in a way that excludes the existence or possibility of one or more other characteristics, numbers, steps, actions, elements, parts or combinations thereof other than those described.

[0027] In the following description, similar terms such as “transfer,” “communicate,” “transmit,” and “receive” of signals or information include not only direct transmission of signals or information from one component to another, but also transmission via other components. In particular, “transferring” or “transmitting” a signal or information to one component indicates the final destination of that signal or information, not the direct destination. The same is true for “receiving” a signal or information. Furthermore, in this specification, two or more pieces of data or information being “related” means that by obtaining one piece of data (or information), it is possible to obtain at least a portion of other pieces of data (or information) based on it.

[0028] On the other hand, embodiments of the present invention may include a program for performing the methods described herein on a computer, and a computer-readable recording medium containing the program. The computer-readable recording medium may include program instructions, local data files, local data structures, etc., individually or in combination. The medium may be specially designed and configured for the present invention, or may be one that is commonly available in the field of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and hardware devices such as ROMs, RAMs, and flash memory that are specially configured to store and execute program instructions. Examples of the program may include not only machine code, such as that produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

[0029] Figure 1 is a block diagram illustrating the configuration of a device that provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to one embodiment of the present invention.

[0030] Firstly, the configuration according to the embodiment of the present invention does not have a physical entity; rather, application programs that perform each function are installed and executed on a single computer or server, or application programs that perform one or more of the configuration's functions are installed on multiple servers and can operate organically through an open network.

[0031] A server, in terms of hardware, has the same configuration as a typical web server. However, in terms of software, it is implemented using a language such as C, C++, Java, Visual Basic, or Visual C++, and includes program modules that perform various functions.

[0032] Furthermore, a computer or server with the aforementioned configuration can be implemented as a web server. A web server generally refers to a computer system and the computer software (web server program) installed for this purpose that is connected to an unspecified number of clients and / or other servers via an open computer network such as the internet, receives work requests from clients or other web servers, and provides the results of those work.

[0033] However, beyond the aforementioned web server program, it should be understood as a broader concept that may include various databases built within a series of application programs that run on the web server.

[0034] In one embodiment, a user's (e.g., medical staff) smart device may have an application installed to provide services offered by a device that provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals. The application may be stored in a computer-readable storage medium of the smart device. The application includes a predetermined set of instructions that can be executed by the smart device's processor. The instructions can cause the smart device's processor to perform operations according to an exemplary embodiment. The computer-readable storage medium of the smart device includes components of an operating system for running the application and other instruction sets on the smart device. For example, such an operating system could be Apple's iOS or Google's Android.

[0035] As shown in Figure 1, an apparatus 100 that provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to one embodiment of the present invention may include a signal acquisition module 200, a signal analysis module 300, and a results provision module 400.

[0036] In one embodiment, the signal acquisition module 200, the signal analysis module 300, and the result provision module 400 may be implemented using one or more physically separated devices, or by one or more processors or a combination of one or more processors and software, and may not be clearly separated in specific operation, unlike the illustrated example.

[0037] Furthermore, in this specification, "module" may mean a functional and structural combination of hardware for carrying out the technical concept of the present invention and software for driving said hardware. For example, the "module" may mean a logical unit of predetermined code and hardware resources for which said predetermined code is performed, and does not necessarily mean physically connected code or a single type of hardware.

[0038] The signal acquisition module 200 can acquire a 12-lead electrocardiogram signal. For example, the signal acquisition module 200 can acquire a 12-lead electrocardiogram signal measured by medical staff through a 12-lead electrocardiogram device.

[0039] On the other hand, a 12-lead electrocardiogram signal can be acquired using 10 skin surface sensors, including four limb leads (right arm (RA), left arm (LA), right bundle branch (RL), and left bundle branch (LL)) and six chest leads (V1, V2, V3, V4, V5, and V6). The 12-lead electrocardiogram signal is characterized by different signal patterns in each lead depending on the type of arrhythmia, and abnormal signs may be observed only in specific leads among the 12 leads.

[0040] The signal acquisition module 200 can provide the acquired 12-lead electrocardiogram signals to the signal analysis module 300.

[0041] The signal analysis module 300 can output analysis results for a 12-lead electrocardiogram (ECG) signal using machine learning-based techniques. For example, the signal analysis module 300 may include an artificial neural network model that has already been trained to classify arrhythmias based on the input 12-lead ECG signal. In this case, the artificial neural network model may be a lightweight model that has been trained on a deep learning network training server. That is, it may be a lightweight artificial neural network model that has been trained to run on an application installed on a smart device.

[0042] On the other hand, a detailed explanation of the operation and configuration of the signal analysis module 300 will be provided later with reference to Figures 2 and 3.

[0043] The results provision module 400 can generate and provide to a user (e.g., medical staff) information necessary for arrhythmia classification and diagnosis based on the analysis results output from the signal analysis module 300. Specifically, the results provision module 400 can generate information necessary for arrhythmia classification and diagnosis, including the type of arrhythmia classified based on the analysis results, the estimated probability of that type of arrhythmia, and the input 12-lead electrocardiogram signal. In this case, the 12-lead electrocardiogram signal may be in a state where the PQRST wave is emphasized. For example, as shown in Figure 4, the results provision module 400 can provide the user with the 12-lead electrocardiogram signal along with the type of arrhythmia the patient will be diagnosed with and the probability of being diagnosed with that arrhythmia, based on the analysis results, by displaying it through the smart device's display. In other words, by providing the 12-lead electrocardiogram signal (with the PQRST wave emphasized) based on arrhythmia classification and diagnosis, along with the type of arrhythmia and the probability of being diagnosed with that arrhythmia, the problem of reduced accuracy due to the lightweight artificial neural network model can be solved.

[0044] Therefore, a device that provides information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to one embodiment of the present invention can reduce the time required for medical professionals (cardiology specialists) to determine arrhythmias by providing information necessary for arrhythmia classification and diagnosis from a standard 12-lead electrocardiogram signal to a smart device using machine learning technology.

[0045] Figure 2 is a block diagram illustrating the configuration of a signal analysis module constituting a device that provides information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to one embodiment of the present invention, and Figure 3 is a schematic diagram showing the structure of a signal analysis module constituting a device that provides information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to one embodiment of the present invention.

[0046] The signal analysis module 300 shown in Figures 2 and 3 may include an Initial Feature Block 310, an Attention Block 320, a Residual Block 330, a Sum Block 340, and a Classification Block 350.

[0047] The Initial Feature Block 310 can output an initial feature map through convolution operations based on the input 12-lead electrocardiogram signal.

[0048] In an exemplary embodiment, the Initial Feature Block 310 may include a Convolution Layer, an Activation Layer (PReLU), and an Average Pooling Layer. That is, the Initial Feature Block 310 can output an initial feature map from a 12-lead electrocardiogram signal through the Convolution Layer, Activation Layer, and Average Pooling Layer. Here, the Activation Layer can use the PReLU (Parametric ReLU) function as its activation function. An activation function is necessary to readjust the signal intensity of a neuron, and the PReLU function outputs values ​​less than 0 multiplied by a parameter (a) which is adjusted through learning, and outputs values ​​greater than 0 as the input value.

[0049] The attention block 320 can output a focus feature map through element-wise weight calculations based on the initial feature map output from the initial feature block 310.

[0050] In an exemplary embodiment, as shown in Figure 5, the attention block 320 may include a convolutional layer, a Max Pooling Layer, an Average Pooling Layer, a Dilated Convolution Layer, a Activation Layer, and a Combined Layer.

[0051] Specifically, attention block 320 can perform max pooling and average pooling in parallel and simultaneously through the max pooling layer and average pooling layer. Attention block 320 can also perform an extended convolution operation on each pooling result calculated through the max pooling layer and average pooling layer, and combine the results through the first join layer (element-wise sum). Here, the output value as the result of the operation may be a weight feature map (i.e., element-wise weight values). Attention block 320 can also perform an operation (element-wise multiplication) to combine the initial feature map and the weight feature map through the second join layer. Attention block 320 can also output a condensed feature map through an activation function (ReLU) as the result of the operation. In this case, before performing the operation through the second join layer, attention block 320 can prevent overfitting by ensuring that the element-wise weight values ​​of the weight feature map are in the range of 0 to 1 through an activation layer (Sigmoid Function) and dropout.

[0052] In other words, the attention block 320 considers the spacing between elements using max pooling, average pooling, and extended convolution operations, assigns weight values ​​to each element according to its importance, outputs a weighted feature map, and allows for the emphasis of important elements among the elements through the combination of the weighted feature map and the initial feature map. Here, elements can be pixels in the feature map. On the other hand, in this invention, the dilation rate of the extended convolution layer is set to 2, but is not limited to this.

[0053] The residual block 330 can output a deep feature map through a shortcut operation based on the initial feature map output from the initial feature block 310.

[0054] In an exemplary embodiment, the residual block 330 may include a plurality of short residual blocks. As shown in Figure 6, the short residual block may include a plurality of convolutional layers, a plurality of batch normalization layers, an activation layer (ReLU), and a join layer.

[0055] In other words, each short residual block outputs a new feature map from the previous feature map (the feature map output from the N-1th short attention block, in which case the first short attention block's initial feature map is the previous feature map) through a convolutional layer, and the previous and new feature maps can be combined (element-wise sum) through a join layer. Therefore, each short residual block can solve the problem of feature information disappearing while extracting deep features by combining the new feature map with the previous feature map, thereby ensuring a smooth flow of information among all short residual blocks of the residual block 330. On the other hand, overfitting can be prevented by applying dropout to the feature map output from the short residual block activation layer.

[0056] The sum block 340 can combine the lumped feature map output from the attention block 320 and the deep feature map output from the residual block 330 to output a final feature map. In this case, the sum block 340 can use element-wise sum to combine the lumped feature map and the final feature map.

[0057] The Classification Block 350 can classify the types of arrhythmias based on the final feature map output from the Combination Block 340. In this case, the Classification Block 350 estimates the probability for each class (type of arrhythmia) based on the final feature map and can classify the type of arrhythmia into the class with the highest probability.

[0058] In an exemplary embodiment, the classification block 350 can perform global max pooling and global average pooling in parallel and simultaneously through a global max pooling layer and a global average pooling layer. The classification block 350 can also perform an operation to concatenate the pooling results calculated through the global max pooling layer and the global average layer through a concatenation layer. This allows the output data of the mutually separate layers to be combined into a single output. By using the global max pooling layer and the global average pooling layer, the final feature map can be output as a pre-set number of one-dimensional matrix feature maps. Furthermore, the classification block 350 can estimate probabilities for each class through a fully connected layer using the output pre-set number of one-dimensional matrix feature maps, and classify the class with the highest probability into a type of arrhythmia. Here, a Softmax function or the like can be used as the fully connected layer. The pre-set number here may be the number of arrhythmia types.

[0059] Figure 7 is a flowchart illustrating a method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to one embodiment of the present invention. The method shown in Figure 7 can be performed, for example, by the aforementioned device for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals. In the illustrated flowchart, the method is described in several steps, but at least some of the steps can be performed in a different order, combined with other steps, omitted, divided into more detailed steps, or with the addition of one or more steps not shown.

[0060] The device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, is an S710 that acquires 12-lead electrocardiogram signals. For example, the device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, can acquire 12-lead electrocardiogram signals measured by medical staff through a 12-lead electrocardiogram device.

[0061] Next, the device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, outputs analysis results for the 12-lead electrocardiogram signals using machine learning-based techniques from the 12-lead electrocardiogram signals S720. For example, the device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, may include an artificial neural network model that has already been trained to classify arrhythmias based on the input 12-lead electrocardiogram signals.

[0062] Next, the device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, generates information necessary for arrhythmia classification and diagnosis based on the output analysis results and provides it to the user (e.g., medical staff) S730. For example, the device 100, which provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, may provide the user with the type of arrhythmia the patient will be diagnosed with and the probability of being diagnosed with that arrhythmia, along with the 12-lead electrocardiogram signal, through the display unit of a smart device.

[0063] Figure 8 is a block diagram illustrating a computing environment, including computing equipment suitable for use in an exemplary embodiment. In the illustrated embodiment, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those described below.

[0064] The illustrated computing environment 10 includes a computing device 12. In one embodiment, the computing device 12 may be a device 100 that provides information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals.

[0065] The computing device 12 includes at least one processor 14, a computer-readable storage medium 16, and a communication bus 18. The processor 14 can be operated by the computing device 12 in accordance with the exemplary embodiments described above. For example, the processor 14 can execute one or more programs stored in the computer-readable storage medium 16. The one or more programs may include one or more computer-executable instructions, which, when executed by the processor 14, can be configured by the computing device 12 to operate in accordance with the exemplary embodiments.

[0066] The computer-readable storage medium 16 is configured to store computer-executable instructions or program code, program data, and / or other suitable forms of information. The program 20 stored in the computer-readable storage medium 16 includes a set of instructions that can be executed by the processor 14. In one embodiment, the computer-readable storage medium 16 may be memory (volatile memory such as random-access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media that can be accessed by the computing device 12 and can store desired information, or a suitable combination thereof.

[0067] The communication bus 18 includes a processor 14 and a computer-readable storage medium 16, and interconnects various other components of the computing device 12.

[0068] The computing device 12 may also include one or more input / output interfaces 22 and one or more network communication interfaces 26 that provide interfaces for one or more input / output devices 24. The input / output interfaces 22 and network communication interfaces 26 are connected to a communication bus 18. The input / output devices 24 may be connected to other components of the computing device 12 via the input / output interfaces 22. Exemplary input / output devices 24 may include input devices such as pointing devices (such as a mouse or trackpad), keyboards, touch input devices (such as a touchpad or touchscreen), voice or sound input devices, various types of sensor devices and / or imaging devices, and / or output devices such as display devices, printers, speakers and / or network cards. Exemplary input / output devices 24 may be included inside the computing device 12 as one component of the computing device 12, or they may be connected to the computing device 12 as separate devices distinct from the computing device 12.

[0069] Although typical embodiments of the present invention have been described in detail above, those with ordinary skill in the art to which the present invention pertains will understand that various modifications are possible to the embodiments described above without departing from the scope of the present invention. Therefore, the scope of the rights of the present invention should not be limited to the embodiments described, but should be defined not only by the claims described below, but also by equivalent claims or the like. [Note 1] One or more processors, A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, performed on a computing device having memory for storing one or more programs executed by one or more processors, The steps include acquiring a 12-lead electrocardiogram signal, The steps include: outputting analysis results for the 12-lead electrocardiogram signal using machine learning-based technology from the aforementioned 12-lead electrocardiogram signal; A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, comprising the step of generating information necessary for arrhythmia classification and diagnosis based on the aforementioned analysis results. [Note 2] The step of outputting the classification result is: A method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal as described in Appendix 1, which receives the input of the 12-lead electrocardiogram signal through a pre-trained artificial neural network model and classifies the type of arrhythmia based on the 12-lead electrocardiogram signal. [Note 3] The aforementioned classification step is, The initial feature block outputs an initial feature map through a convolution operation based on the input 12-lead electrocardiogram signal, The attention block includes the step of outputting a concentrated feature map through element-wise weight calculations based on the output initial feature map, The residual block provides a step of outputting a deep feature map through a shortcut operation based on the output initial feature map, The process involves a merging block to combine the output condensed feature map and the output deep feature map to output a final feature map, The classification block further includes the step of estimating the probability for a pre-defined class based on the output final feature map, and classifying the class with the highest probability into a type of arrhythmia. The information necessary for the classification and diagnosis of arrhythmias is a method for providing information necessary for the classification and diagnosis of arrhythmias using a 12-lead electrocardiogram signal as described in Appendix 2, which includes the type of arrhythmia classified, the estimated probability, and the input 12-lead electrocardiogram signal. [Note 4] The step of outputting the aforementioned concentrated feature map is: The steps include performing maximum pooling and average pooling in parallel on the initial feature map through a maximum pooling layer and an average pooling layer, The steps include: combining the pooling results output through the maximum pooling layer and the average pooling layer through a first join layer to output a weight feature map; A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals as described in Appendix 3, further comprising the step of combining the initial feature map and the weight feature map through a second coupling layer to output the concentrated feature map. [Note 5] The aforementioned residual block is, It is configured to include N short residual blocks (where N is a natural number greater than or equal to 2) that are sequentially linked to reflect the features of the initial feature map, The aforementioned N short residual blocks are, A method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal as described in Appendix 4, wherein a previous feature map (a feature map output from the N-1 short residual block) is input, a new feature map is output from the previous feature map through a convolutional layer, and the previous feature map and the new feature map are combined and output through a third combining layer. [Note 6] The first and third join layers use element-wise sums. The second coupling layer is a method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals as described in Appendix 5, using element-wise multiplication. [Note 7] The step of classifying the type of arrhythmia is, The steps include performing global maximum pooling and global average pooling in parallel on the aforementioned final feature map through a global maximum pooling layer and a global average pooling layer, A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals as described in Appendix 3, further comprising the step of concatenating each pooling result output through the global maximum pooling layer and the global average pooling layer through a concatenation layer. [Note 8] One or more processors, A computing device having memory for storing one or more programs executed by one or more processors, A signal acquisition module that acquires 12-lead electrocardiogram signals, A signal analysis module that outputs analysis results for the 12-lead electrocardiogram signal using machine learning-based technology from the aforementioned 12-lead electrocardiogram signal, A computing device including a results-providing module that generates information necessary for arrhythmia classification and diagnosis based on the aforementioned analysis results. [Note 9] The aforementioned classification module is The computing device described in Appendix 8, which includes an artificial neural network model that has already been trained to receive the 12-lead electrocardiogram signal input and classify arrhythmia information based on the 12-lead electrocardiogram signal. [Note 10] The aforementioned artificial neural network model is An initial feature block that outputs an initial feature map through a convolution operation based on the input 12-lead electrocardiogram signal, A attention block that outputs a concentrated feature map through element-wise weight calculations based on the initial feature map output from the initial feature block, A residual block that outputs a deep feature map through shortcut calculations based on the initial feature map output from the initial feature block, A merging block that combines the concentrated feature map output from the attention block and the deep feature map output from the residual block to output a final feature map, A classification block that estimates the probability for a pre-defined class based on the final feature map output from the aforementioned combined block, and classifies the class with the highest probability into a type of arrhythmia, is included. The computing device described in Appendix 9 includes the type of arrhythmia classified, the estimated probability, and the input 12-lead electrocardiogram signal, as necessary for the classification and diagnosis of the arrhythmia. [Note 11] The aforementioned warning block is, The computing device according to Appendix 10, which performs maximum pooling and average pooling in parallel on the initial feature map output from the initial feature block through a maximum pooling layer and an average pooling layer, combines the pooling results output through the maximum pooling layer and the average pooling layer through a first join layer to output a weight feature map, and combines the initial feature map and the weight feature map through a second join layer to output the concentrated feature map. [Note 12] The aforementioned residual block is, It is configured to include N short residual blocks (where N is a natural number greater than or equal to 2) that are sequentially linked to reflect the features of the initial feature map output from the initial feature block, The aforementioned N short residual blocks are, The computing device described in Appendix 11, which takes a previous feature map (a feature map output from the N-1th short residual block) as input, outputs a new feature map from the previous feature map through a convolutional layer, and outputs the previous feature map and the new feature map combined through a third joining layer. [Note 13] The first and third join layers use element-wise sums. The second joining layer is a computing device as described in Appendix 12 that uses element-wise multiplication. [Note 14] The aforementioned classification block is, The computing device according to Appendix 10, which performs global maximum pooling and global average pooling in parallel on the final feature map through a global maximum pooling layer and a global average pooling layer, and concatenates the pooling results output through the global maximum pooling layer and the global average pooling layer through a concatenation layer. [Explanation of symbols]

[0070] 10: Computing Environment 12: Computing devices 14: Processor 16: Computer-readable storage media 18: Communications bus 20: Program 22: Input / Output Interface 24: Input / Output Devices 26: Network communication interface A device that provides information necessary for arrhythmia classification and diagnosis using 100:12 lead electrocardiogram signals. 200: Signal acquisition module 300: Signal Analysis Module 310: Initial Feature Block 320: Warning Block 330: Residual Block 340: Joining Block 350: Classification Block 400: Result-Providing Module

Claims

1. One or more processors, A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals, performed on a computing device equipped with memory for storing one or more programs executed by one or more processors, The steps include acquiring a 12-lead electrocardiogram signal, The steps include: outputting analysis results for the 12-lead electrocardiogram signal using machine learning-based technology from the 12-lead electrocardiogram signal; The step includes generating information necessary for arrhythmia classification and diagnosis based on the analysis results, The step of outputting the analysis results is: The system receives the input of the 12-lead electrocardiogram signal through a pre-trained artificial neural network model, and classifies the type of arrhythmia based on the 12-lead electrocardiogram signal. The aforementioned classification step is, The initial feature block outputs an initial feature map through a convolution operation based on the input 12-lead electrocardiogram signal, The attention block includes the step of outputting a concentrated feature map through element-wise weight calculations based on the output initial feature map, The residual block provides a step of outputting a deep feature map through a shortcut operation based on the output initial feature map, The process involves a merging block to combine the output condensed feature map and the output deep feature map to output a final feature map, The classification block further includes the step of estimating the probability for a pre-defined class based on the output final feature map, and classifying the class with the highest probability into a type of arrhythmia. A method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal, wherein the information necessary for arrhythmia classification and diagnosis includes the type of arrhythmia classified, the estimated probability, and the input 12-lead electrocardiogram signal.

2. The step of outputting the aforementioned concentrated feature map is: The steps include performing maximum pooling and average pooling in parallel on the initial feature map through a maximum pooling layer and an average pooling layer, The steps include: combining the pooling results output through the maximum pooling layer and the average pooling layer through a first join layer to output a weight feature map; A method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to claim 1, further comprising the step of combining the initial feature map and the weight feature map through a second combining layer to output the concentrated feature map.

3. The aforementioned residual block is, It is configured to include N short residual blocks (where N is a natural number greater than or equal to 2) that are sequentially linked to reflect the features of the initial feature map, The aforementioned N short residual blocks are, A method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to claim 2, wherein a previous feature map (a feature map output from the N-1 short residual block) is input, a new feature map is output from the previous feature map through a convolutional layer, and the previous feature map and the new feature map are combined and output through a third combining layer.

4. The first and third join layers use element-wise sums, The method for providing information necessary for arrhythmia classification and diagnosis using a 12-lead electrocardiogram signal according to claim 3, wherein the second coupling layer uses element-wise multiplication.

5. The step of classifying the type of arrhythmia is, The steps include performing global maximum pooling and global average pooling in parallel on the aforementioned final feature map through a global maximum pooling layer and a global average pooling layer, A method for providing information necessary for arrhythmia classification and diagnosis using 12-lead electrocardiogram signals according to claim 1, further comprising the step of concatenating each pooling result output through the global maximum pooling layer and the global average pooling layer through a concatenation layer.

6. One or more processors, A computing device having memory for storing one or more programs executed by one or more processors, A signal acquisition module that acquires 12-lead electrocardiogram signals, A signal analysis module that outputs analysis results for the 12-lead electrocardiogram signal using machine learning-based technology, Includes a results-providing module that generates information necessary for arrhythmia classification and diagnosis based on the aforementioned analysis results, The aforementioned signal analysis module is It includes an artificial neural network model that has already been trained to receive the input of the 12-lead electrocardiogram signal and classify arrhythmia information based on the 12-lead electrocardiogram signal, The aforementioned artificial neural network model is An initial feature block that outputs an initial feature map through a convolution operation based on the input 12-lead electrocardiogram signal, A attention block that outputs a concentrated feature map through element-wise weight calculations based on the initial feature map output from the initial feature block, A residual block that outputs a deep feature map through shortcut calculations based on the initial feature map output from the initial feature block, A merging block that combines the concentrated feature map output from the attention block and the deep feature map output from the residual block to output a final feature map, A classification block that estimates the probability for a pre-defined class based on the final feature map output from the aforementioned combined block, and classifies the class with the highest probability into a type of arrhythmia, is included. The information necessary for the classification and diagnosis of arrhythmias includes the type of arrhythmia classified, the estimated probability, and the input 12-lead electrocardiogram signal, as well as a computing device.

7. The aforementioned warning block is, The computing device according to claim 6, wherein maximum pooling and average pooling are performed in parallel on the initial feature map output from the initial feature block through a maximum pooling layer and an average pooling layer, the pooling results output through the maximum pooling layer and the average pooling layer are combined through a first combine layer to output a weight feature map, and the initial feature map and the weight feature map are combined through a second combine layer to output the concentrated feature map.

8. The aforementioned residual block is, It is configured to include N short residual blocks (where N is a natural number of 2 or more) that are sequentially linked to reflect the features of the initial feature map output from the initial feature block, The aforementioned N short residual blocks are, The computing device according to claim 7, wherein a previous feature map (a feature map output from the N-1th short residual block) is input, a new feature map is output from the previous feature map through a convolutional layer, and the previous feature map and the new feature map are combined and output through a third joining layer.

9. The first and third join layers use element-wise sums, The computing device according to claim 8, wherein the second combining layer uses element-wise multiplication.

10. The aforementioned classification block is, The computing device according to claim 6, wherein global maximum pooling and global average pooling are performed in parallel on the final feature map through a global maximum pooling layer and a global average pooling layer, and the pooling results output through the global maximum pooling layer and the global average pooling layer are concatenated through a concatenation layer.

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