Method for integrating electrocardiogram descriptions and results

Abstract

According to an embodiment of the present disclosure, a method for processing medical data and a computing device using the same are disclosed. Specifically, according to the present disclosure, a computing device receives one or more electrocardiogram data, generates electrocardiogram delineation information corresponding to a plurality of samples generated from the one or more received electrocardiogram data using an electrocardiogram description model, and integrates the plurality of electrocardiogram delineation information.

Description

Technical Field

[0001] The present disclosure relates to a method for processing medical data, and more specifically, to a method for generating and integrating electrocardiogram (ECG) delineation information related to ECG data measured from one or more leads.

Background Art

[0002] The waveform of an electrocardiogram (ECG) that reflects the electrical activation stages of the heart is basically composed of the detailed regions of the P wave, QRS complex, and T wave. The P wave of each heartbeat occurs during the phase when the atrium depolarizes, the QRS complex reflects the phase when the ventricle depolarizes, and the T wave reflects the phase when the ventricle repolarizes.

[0003] ECG delineation is a signal processing process that separates the above-mentioned detailed regions from ECG data. In ECG analysis, ECG delineation occupies a very important position. The reason is that by obtaining the detailed characteristics of the heartbeat from the results of ECG delineation and obtaining information related to relative time intervals such as the amplitude of the detailed region, PR interval, ST interval, and QT interval, it is possible to grasp the abnormality of the heartbeat rhythm and the presence or absence of heart diseases.

[0004] However, the types of ECG measurement equipment have diversified (Standard 12-Lead, Holter, Mobile single-lead, Limb lead), and due to differences in the types and numbers of leads used for ECG measurement and the length of the measurement time, differences occur between the multiple data received for each piece of equipment, making it difficult to stably derive ECG delineation information. U.S. Patent Publication No. 2021-0219920 discloses a system and method for internal ECG acquisition.

Summary of the Invention

[0005] This disclosure was devised in response to the aforementioned background technology and aims to provide a method for deriving stable electrocardiogram descriptive information regardless of the number of leads of the measurement equipment and the length of the measurement time. [Means for solving the problem]

[0006] Based on one embodiment of the present disclosure for achieving the aforementioned objectives, a method for electrocardiogram description performed by one or more processors of a computing device is disclosed. The method may include: receiving one or more electrocardiogram data; generating electrocardiogram description (ECG delineation) information corresponding to multiple samples, each having a predetermined time length, from the received one or more electrocardiogram data using an electrocardiogram description model; and integrating the multiple electrocardiogram description information.

[0007] In the alternative embodiment, the step of receiving the electrocardiogram data may include the step of receiving one or more electrocardiogram data from one of several electrocardiogram measurement devices of different types.

[0008] In the alternative embodiment, the steps of generating electrocardiogram description information corresponding to multiple samples, each having a predetermined time length, from one or more electrocardiogram data using the electrocardiogram description model described above may include: a step of confirming the number of leads of the electrocardiogram measurement equipment and the length of the measurement time; a step of generating the multiple samples by dividing one or more electrocardiogram data based on the predetermined time length; and a step of generating each electrocardiogram description information corresponding to the multiple samples using the electrocardiogram description model described above.

[0009] In the alternative embodiment, the length of the preset time can be determined independently of the number of leads of the electrocardiogram measurement equipment or the length of the measurement time.

[0010] In the alternative embodiment, the electrocardiogram description information includes at least one of the following: P-wave position information; QRS complex position information; or T-wave position information; however, the processor is capable of deriving at least one of the P-wave amplitude information; QRS complex amplitude information; or T-wave amplitude information from the electrocardiogram description information.

[0011] In the alternative embodiment, the step of integrating the multiple electrocardiogram description information may include: a step of generating smoothed electrocardiogram description information relating to the multiple samples; and a step of integrating the smoothed electrocardiogram description information relating to the multiple samples.

[0012] In the alternative embodiment, the step of generating smoothed electrocardiogram description information for the above-mentioned multiple samples may further include a step of deriving a representative value from among multiple result values ​​of the electrocardiogram description information for the above-mentioned multiple samples within a fixed time interval.

[0013] In the alternative embodiment, the step of integrating the smoothed electrocardiogram description information relating to the multiple samples may further include the step of deriving a representative value from among the multiple result values ​​of the smoothed electrocardiogram description information.

[0014] In an alternative embodiment, the disclosure may further include a step of extracting electrocardiogram characteristics from the integrated electrocardiogram description information. The step of extracting electrocardiogram characteristics from the integrated electrocardiogram description information may include a step of dividing the integrated electrocardiogram description information based on the duration of a single heartbeat; a step of extracting each electrocardiogram characteristic from the electrocardiogram description information divided for each duration of a single heartbeat; and a step of deriving a representative value for each extracted electrocardiogram characteristic.

[0015] In the alternative embodiment, the electrocardiogram characteristics described above may include at least one of the following: RR-interval; QRS duration; QT interval; QT-corrected interval; PR-segment; PR-interval; ST-segment; or TP-segment.

[0016] In an alternative embodiment, the electrocardiogram description method of this disclosure further includes a step of generating a user interface (UI) that includes information relating to the electrocardiogram description information, the user interface may include a first area for displaying electrocardiogram data measured by the electrocardiogram measurement equipment; a second area for displaying the electrocardiogram description information; and a third area for displaying electrocardiogram characteristic values.

[0017] Based on the alternative embodiments of this disclosure, a computer program stored on a computer-readable storage medium is disclosed which includes instructions causing a computing device to perform an operation. The operation may include: receiving one or more electrocardiogram data; generating electrocardiogram description information from the one or more received electrocardiogram data, each corresponding to a set number of samples having a predetermined time length; and integrating the multiple electrocardiogram description information.

[0018] A computing device is disclosed based on an alternative embodiment in this disclosure. The computing device may include: a processor having one or more cores; a network unit for receiving one or more electrocardiogram data; and memory, wherein the processor may include a process for receiving one or more electrocardiogram data, generating electrocardiogram description information corresponding to multiple samples having a predetermined time length from the one or more electrocardiogram data using an electrocardiogram description model, and integrating the electrocardiogram description information. [Effects of the Invention]

[0019] The present disclosure can stably provide electrocardiogram description information regardless of the number of leads and the length of the measurement time.

Brief Description of the Drawings

[0020] The following drawings attached for use in the description of the embodiments of the present disclosure are merely a part of the embodiments of the present disclosure. For those with ordinary knowledge in the technical field to which the present disclosure belongs (hereinafter referred to as "ordinary technicians"), it is possible to obtain other drawings based on these drawings without making efforts to conceive of a new invention. [Figure 1] FIG. 1 is a block configuration diagram of a computing device that performs operations for generating electrocardiogram description information based on an embodiment of the present disclosure. [Figure 2] FIG. 2 is a schematic diagram showing a network function based on an embodiment of the present disclosure. [Figure 3] FIG. 3 is a flowchart relating to a method for generating and integrating electrocardiogram description information based on an embodiment of the present disclosure. [Figure 4] FIG. 4 is a block configuration diagram showing a process for generating electrocardiogram description information of a computing device based on an embodiment of the present disclosure. [Figure 5] FIG. 5 is a conceptual diagram showing a process for smoothing electrocardiogram description information based on an embodiment of the present disclosure. [Figure 6] FIG. 6 is a conceptual diagram showing electrocardiogram characteristics based on an embodiment of the present disclosure. [Figure 7] FIG. 7 is a block configuration diagram of a computing device based on an embodiment of the present disclosure.

Modes for Carrying Out the Invention

[0021] The present disclosure discloses a method for receiving electrocardiogram data of a patient measured by an electrocardiogram measuring device, stably generating electrocardiogram description information regardless of the number of leads and the length of the measurement time, deriving an integrated result, and extracting electrocardiogram characteristics from the result.

[0022] Various embodiments will be described below with reference to the drawings, and like drawing numbers are used throughout the drawings to represent like components. Various explanations are presented herein to facilitate the understanding of the present disclosure. However, these embodiments can be implemented without these specific explanations. As used herein, terms such as "component", "module", "system", etc. refer to computer-related entities, hardware, firmware, software, combinations of software and hardware, or the execution of software. For example, a component can be a processing procedure (procedure) executed on a processor, a processor, an object, an execution thread, a program, and / or a computer, but is not limited thereto. For example, an application executed on a computing device and the computing device can both be components. One or more components can reside within a processor and / or an execution thread, one component can be localized within one computer, or can be distributed among two or more computers. Also, such components can be executed from various computer-readable media having various data structures stored therein. Components can communicate through local and / or remote processing, for example, by signals having one or more data packets (e.g., data and / or signals from one component interacting with other components in a local system, a distributed system, and transmitted through a network such as the Internet to other systems).

[0023] The term "or" is used with the intention of meaning an implicational "or," not an exclusive "or." That is, unless specifically specified and contextually clear, "X uses A or B" means one of the natural implicational substitutions. In other words, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" can be any of these. Furthermore, the terms "and / or" in this specification should be understood to refer to and include all possible combinations of one or more of the related items discussed.

[0024] Furthermore, the term “includes” as a predicate and / or as a modifier should be understood to mean that the feature and / or component in question exists. However, the term “includes” as a predicate and / or as a modifier should be understood not to exclude the existence or addition of one or more other further features, components and / or groups thereof. Also, where the number is not specifically identified or where it is not clear from the context to indicate a singular form, “singular” in this specification and claims should generally be interpreted to mean “one or more.”

[0025] Furthermore, the phrase "at least one of A or B" should be interpreted as meaning "including only A," "including only B," or "a combination of A and B."

[0026] Those skilled in the art should further recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logic, and algorithmic stages described herein as relating to the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interoperability between hardware and software, various exemplary components, blocks, configurations, means, logic, modules, circuits, and stages have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design constraints of the overall system. Skilled technicians can implement the described functionality in various ways for individual specific applications; however, decisions regarding such implementation should not be construed as departing from the scope of this disclosure.

[0027] The descriptions relating to the embodiments shown herein are provided so that a person with ordinary skill in the art of this disclosure may utilize or practice the invention. Various modifications to such embodiments are obvious to a person with ordinary skill in the art of this disclosure, and the general principles defined herein can be applied to other embodiments without departing from the scope of this disclosure. Therefore, this disclosure is not limited by the embodiments shown herein, but should be interpreted in the broadest sense consistent with the principles and novel features shown herein. In this disclosure, network functions, artificial neural networks, and neural networks can be used interchangeably.

[0028] On the other hand, in the detailed description and claims of this disclosure, the terms “image,” “picture,” or “video,” or “image data,” “image data,” or “video data,” refer to multidimensional data composed of discrete image elements (for example, pixels in a two-dimensional image), or in other words, to an object that can be visually recognized (for example, displayed on a video screen) or a digital representation of that object (for example, a file corresponding to the pixel output in a CT or MRI detector).

[0029] For example, “image,” “picture,” or “video” could be a medical image of a subject collected by computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or any other medical imaging system known in the art of the present invention. The image does not necessarily have to be provided for medical purposes and may be provided for non-medical purposes, such as X-rays for security inspections.

[0030] In the detailed description and claims of this disclosure, the term "DICOM (Digital Imaging and Communications in Medicine)" is a general term encompassing all standards used for digital image representation and communications in medical devices, and the DICOM standards are published by a joint committee composed of the American Radiological Society (ACR) and the National Electrical Manufacturers' Association (NEMA).

[0031] Furthermore, in the detailed description and claims of this disclosure, "Picture Archiving and Communication System (PACS)" is a term referring to a system that stores, processes, and transmits medical images in accordance with the DICOM standard. It is possible to store medical image data acquired using digital medical imaging devices such as X-ray, CT, and MRI in DICOM format and transmit it to terminals within or outside the hospital via a network, and to add interpretation results and medical records to that data.

[0032] Figure 1 is a diagram showing a block configuration of a computing device that generates electrocardiogram result description information, based on one embodiment of the present disclosure.

[0033] The configuration of the computing device (100) shown in Figure 1 is merely a simplified example. In one embodiment of this disclosure, the computer device (100) may include other configurations for implementing the computing environment of the computer device (100), and it is also possible to configure the computer device (100) using only some of the disclosed configurations.

[0034] The computer device (100) may include a processor (110), memory (130), and a network unit (150).

[0035] In one embodiment of the present disclosure, the processor (100) may consist of one or more cores and may include processors for data analysis and deep learning, such as a central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU). The processor (110) can read computer programs stored in memory (130) and perform data processing for machine learning in one embodiment of the present disclosure. Based on one embodiment of the present disclosure, the processor (110) can perform calculations for training a neural network. In deep learning (DL), the processor (110) can perform calculations for training a neural network, such as processing input data for training, extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) can process the training of the network function. For example, both the CPU and GPGPU can perform network function training and data classification using network functions. In one embodiment of this disclosure, the processors of multiple computing devices can be used together to perform network function training and data classification using network functions. Furthermore, in one embodiment of this disclosure, the computer program executed on the computing device can be a program that can be executed on the CPU, GPGPU, or TPU.

[0036] The processor (110) can receive one or more electrocardiogram data from multiple electrocardiogram measurement devices of different types and generate electrocardiogram description information. For example, the processor (110) can receive one or more electrocardiogram data from multiple electrocardiogram measurement devices having different numbers of leads or different measurement times, and can generate electrocardiogram description information related to each electrocardiogram measurement device.

[0037] In an alternative embodiment, the processor can generate multiple samples, each containing an ECG data point and a corresponding preset time length, by dividing the ECG data based on a preset time length determined independently of the number of leads or the length of the measurement time of the ECG measurement equipment. In this case, the preset time length can be long enough to be accepted as input by the ECG description model.

[0038] The processor (110) can input multiple divided samples, each having a preset time length, into an electrocardiogram description model and obtain electrocardiogram description information generated for each sample as output. The input to the electrocardiogram description model can be electrocardiogram data of a single fixed length, i.e., a preset time length. The output of the electrocardiogram description model can be electrocardiogram description information, i.e., information including the position information of the P wave, QRS complex, and T wave of all beats contained in the input multiple samples. Here, 'correspondence' can mean that one electrocardiogram description is obtained from each of the multiple samples, and there is a one-to-one correspondence between each sample and the electrocardiogram description. In this case, the number of output electrocardiogram description pieces can be the same as the number of input samples, each having a preset time length. The processor (110) can integrate electrocardiogram description information relating to multiple samples, each having the above-described preset time lengths, into a single electrocardiogram description. For example, after smoothing the electrocardiogram description information relating to multiple samples, the mode at a specific time point can be used as the value of the integrated electrocardiogram description, but this disclosure is not limited to this. A specific explanation of smoothing will be given later with reference to Figure 5.

[0039] The processor (110) can extract electrocardiogram characteristics based on the integrated electrocardiogram description information described above. The electrocardiogram characteristics may include, but are not limited to, the RR-interval, QRS duration, QT interval, or QT-corrected interval, which are the basis for determining abnormal heart rates and cardiac diseases. Since the integrated electrocardiogram description information includes multiple heartbeats, it is necessary to generate electrocardiogram description information divided by the time of each heartbeat in order to extract the electrocardiogram characteristics.

[0040] The processor (110) can divide the integrated electrocardiogram description information based on the duration of a single heartbeat, extract each electrocardiogram characteristic from each of the generated electrocardiogram description pieces divided by the duration of a single heartbeat, and then derive a representative value for each characteristic.

[0041] In one embodiment of the present disclosure, the memory (130) can store information in any form generated or determined by the processor (110) and information in any form received by the network unit (150).

[0042] In one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium from among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. The computing device (100) may also operate in conjunction with web storage that performs the storage function of the memory (130) over the internet. The above descriptions of memory are illustrative and the present disclosure is not limited thereto.

[0043] A network unit (150) according to one embodiment of the present disclosure can use various wired communication systems such as public switched telephone networks (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and local area networks (LANs).

[0044] Furthermore, the network unit (150) presented herein can utilize various wireless communication systems such as CDMA (Code Division Multi Access), TDMA (Time Division Multi Access), FDMA (Frequency Division Multi Access), OFDMA (Orthogonal Frequency Division Multi Access), SC-FDMA (Single Carrier-FDMA), and other systems.

[0045] In one embodiment of this disclosure, the network unit (150) can use any form of wired or wireless communication system.

[0046] The technologies described herein can be used not only in the aforementioned networks but also in other networks.

[0047] The network unit (150) can receive medical images representing body organs from a medical imaging system. For example, medical images representing body organs may be training data or inference data for a neural network model that learns two-dimensional or three-dimensional features. Medical images representing body organs may also be three-dimensional T1 MR images that include at least one brain region. Medical images representing body organs are not limited to the examples above and can include all images related to body organs acquired by imaging, such as X-ray images and CT images.

[0048] Figure 2 is a schematic diagram showing a network function in one embodiment of the present disclosure.

[0049] Throughout this specification, the terms computational model, neural network, neural network model, sub-neural network model, network function, and neural network may be used interchangeably. A neural network can generally consist of a set of interconnected computational units called nodes. Such nodes may also be called neurons. A neural network consists of at least one node. The nodes (or neurons) that make up a neural network can be interconnected by one or more links.

[0050] In a neural network, one or more nodes connected by links can form a relative input-output node relationship. The concepts of input and output nodes are relative; any node that is an output node to one node can be an input node to another, and vice versa. As mentioned above, the relationship between input and output nodes can be established around links. One input node can be connected to one or more output nodes via links, and vice versa. In a relationship between input and output nodes connected via a single link, the output node's data can be determined based on the data input to the input node. The nodes interconnecting the input and output nodes can have weights. These weights can be variable, and can be changed by the user or algorithm to enable the neural network to perform its desired function. For example, if one or more input nodes are interconnected to a single output node via links, the output node's value can be determined based on the values ​​input to the input nodes connected to it and the weights set for the links corresponding to each input node.

[0051] As mentioned above, in a neural network, one or more nodes are interconnected via one or more links, forming an input-output node relationship within the neural network. In a neural network, the characteristics of the network can be determined by the number of nodes and links, the correlation between nodes and links, and the weight values ​​assigned to each link. For example, if there are two neural networks with the same number of nodes and links but different link weight values, these two neural networks can be recognized as distinct.

[0052] A neural network can consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer. Some of the nodes that make up a neural network can form a layer based on their distance from a first input node. For example, a set of nodes that are n in distance from a first input node can form an n-layer. The distance from the first input node can be defined based on the minimum number of links that must be traversed to reach that node from the first input node. However, this definition of a layer is arbitrary for illustrative purposes, and the composition of layers in a neural network can be defined in ways different from those described above. For example, a node layer can also be defined based on its distance from a final output node.

[0053] A first input node can refer to one or more nodes in a neural network that receive data directly without going through links in relation to other nodes. Alternatively, it can refer to nodes in a neural network that do not have other input nodes connected via links in relation to other nodes based on links. Similarly, a final output node can refer to one or more nodes in a neural network that do not have an output node in relation to other nodes. Furthermore, a hidden node can refer to a node that is neither a first input node nor a final output node, but is part of the neural network.

[0054] A neural network according to one embodiment of this disclosure may be a neural network in which the number of nodes in the input layer is the same as the number of nodes in the output layer, and the number of nodes decreases once as you move from the input layer to the hidden layer, and then increases again. A neural network according to one embodiment of this disclosure may be a neural network in which the number of nodes in the input layer is less than the number of nodes in the output layer, and the number of nodes decreases as you move from the input layer to the hidden layer. Furthermore, a neural network according to another embodiment of this disclosure may be a neural network in which the number of nodes in the input layer is greater than the number of nodes in the output layer, and the number of nodes increases as you move from the input layer to the hidden layer. A neural network in another embodiment of this disclosure may be a neural network that combines the neural networks described above.

[0055] A deep neural network (DNN) can be defined as a neural network that includes multiple hidden layers in addition to the input and output layers. By using deep neural networks, it is possible to grasp the latent structures of data. In other words, it is possible to grasp the latent structures of photographs, texts, videos, audio, and music (for example, whether a certain object is in a photograph, what the content and emotions of a text are, what the content and emotions of an audio are, etc.). Deep neural networks can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, Generative Adversarial Networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siam networks, and Generative Adversarial Networks (GANs). The deep neural networks mentioned above are merely examples, and this disclosure is not limited to them.

[0056] In one embodiment of this disclosure, the network function may include an autoencoder. An autoencoder can be a type of artificial neural network for outputting output data similar to input data. An autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between the input and output layers. The number of nodes in each layer may decrease from the number of nodes in the input layer toward an intermediate layer called the bottleneck layer (encoder), and may also expand in a manner that is both contraction and expansion toward the output layer (which is symmetric to the input layer). An autoencoder can perform nonlinear dimensionality reduction. The number of input and output layers can correspond to the dimensions after preprocessing of the input data. In an autoencoder structure, the number of nodes in the hidden layers included in the encoder may decrease as they move further away from the input data. The number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) may be maintained above a certain number (e.g., more than half the number of nodes in the input layer) because if the number of nodes is too small, a sufficient amount of information may not be transmitted.

[0057] Neural networks can be trained using at least one of the following methods: supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Training a neural network can be the process of providing the neural network with the knowledge necessary for it to perform specific actions.

[0058] Neural networks can be trained to minimize the error in their output. During neural network training, training data is repeatedly input into the network. The network's output and target error for the training data are calculated, and the error is backpropagated from the output layer to the input layer to update the weights of each node in the neural network, aiming to reduce the error. In supervised learning, training data with the correct answer labeled is used (i.e., labeled training data), while in unsupervised learning, the correct answer may not be labeled for each training data point. For example, in supervised learning for data classification, the training data could be data with a category labeled for each data point. Labeled training data is input into the neural network, and the error can be calculated by comparing the neural network's output (category) with the labels on the training data. As another example, in unsupervised learning for data classification, the error can be calculated by comparing the input training data with the output of the neural network. The calculated error is backpropagated in the neural network in the reverse direction (i.e., from the output layer to the input layer), and through backpropagation, the connection weights of each node in each layer of the neural network can be updated. The amount of change in the connection weight of each node that is updated can be determined by the learning rate. The calculation of the neural network on the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied in a way that changes depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of learning the neural network, a high learning rate can be used to increase efficiency by enabling the neural network to quickly achieve a certain level of performance, while in the later stages of learning, the learning rate can be lowered to improve accuracy.

[0059] In neural network training, training data can generally be a subset of real-world data (i.e., data that the trained neural network intends to process). Therefore, there can be training cycles where errors related to training data decrease, but errors related to real-world data increase. Overfitting is a phenomenon where errors increase on real-world data due to excessive training on training data. For example, a neural network that has learned to recognize cats by seeing yellow cats may fail to recognize cats of other colors as cats; this is a type of overfitting. Overfitting can cause an increase in errors in machine learning algorithms. Various optimization methods can be applied to prevent such overfitting. Methods that can be applied to prevent overfitting include increasing the amount of training data, regularization, dropout (deactivating some of the network nodes during the training process), and the use of a batch normalization layer.

[0060] Figure 3 is a flowchart relating to a method for generating and integrating electrocardiogram description information based on one embodiment of the present disclosure.

[0061] In stage S310, the processor (110) of the computing device (100) is capable of receiving at least one electrocardiogram data from the electrocardiogram measuring device. The electrocardiogram data may consist of one or more leads calculated based on the potential difference between two electrodes in the electrocardiogram measuring device. Here, the electrocardiogram data may mean data received to the electrocardiogram measuring device via the electrodes of the electrocardiogram measuring device, or data received from electrodes directly connected to the computing device (100).

[0062] In step S320, the processor (110) can divide the electrocardiogram data received in step S310 into multiple samples of a certain length and generate electrocardiogram description information for each of the divided samples. Here, the length of the multiple samples can be such that the electrocardiogram description model for generating the electrocardiogram description information can analyze it.

[0063] In step S330, the processor (110) can integrate multiple ECG description information related to the divided samples into one and generate a single ECG description. In this case, the processor (110) can also perform a smoothing process on each of the divided samples to generate a single ECG description. The specific smoothing process will be described later with reference to Figure 5.

[0064] Figure 4 is a block diagram showing the process for generating electrocardiogram description information of a computing device, based on one embodiment of the present disclosure.

[0065] Electrocardiogram data (411) measured by one or more leads is received from one of several different types of electrocardiogram measurement devices. All of the received electrocardiogram data (410) can be divided into lengths that can be analyzed by the electrocardiogram description model (420) to generate multiple samples. The electrocardiogram description model can be, but is not limited to, a deep learning model trained on an electrocardiogram data set that contains one or more labeling pairs from the electrocardiogram description information.

[0066] Multiple divided electrocardiogram data samples (412) are analyzed by an electrocardiogram description model (420), and it is possible to generate electrocardiogram description information (431) corresponding to each of the multiple samples. Each of the electrocardiogram description information (431) may include P-wave position information, QRS complex position information, or T-wave position information.

[0067] On the other hand, the processor (110) can secondarily derive P-wave amplitude information, QRS complex amplitude information, or T-wave amplitude information from the electrocardiogram description information (431) relating to each of the above-mentioned multiple samples, but the disclosure is not limited thereto.

[0068] On the other hand, the processor (110) performs an integration process on the set of electrocardiogram description information (430) relating to multiple samples after smoothing. Through the integration process, a single electrocardiogram description (440) can be generated. The specific process of smoothing will be described later with reference to Figure 5.

[0069] On the other hand, electrocardiogram characteristics can be extracted from the electrocardiogram description information (440). A detailed explanation of electrocardiogram characteristics will be given later using Figure 6. The integrated electrocardiogram description information (440) can contain multiple heartbeats, and the values ​​of the electrocardiogram characteristics can change slightly for each heartbeat. Therefore, in order to facilitate diagnosis of patients, it is possible to extract electrocardiogram characteristics on a heartbeat basis from the integrated electrocardiogram description information and extract representative values ​​of the electrocardiogram characteristics from those extracted. For example, in order to extract representative values, it is possible to calculate the average for each electrocardiogram characteristic on a heartbeat basis, remove the characteristics that are farther away from the average than a multiple of the standard deviation of the electrocardiogram characteristics on a heartbeat basis, calculate the average again, and use that value as the representative value of each electrocardiogram characteristic.

[0070] On the other hand, in the examples mentioned above, the method for extracting electrocardiogram characteristics can change if there is an abnormality in heart rhythm or heart disease. For example, if an electrocardiogram is measured from a patient with atrial fibrillation, the value of the PR interval should not be defined in the electrocardiogram characteristics because there is no R-peak among the R-wave characteristics in the patient's electrocardiogram. Therefore, in such cases, the ratio of the number of P-waves to the number of R-peaks can be calculated, and if that value does not exceed a threshold, it is possible to define that the PR interval does not exist among the electrocardiogram characteristics.

[0071] Figure 5 is a conceptual diagram showing one electrocardiogram description information smoothing process based on one embodiment of the present disclosure. Referring to Figure 5, the processor (110) can smooth the electrocardiogram description information relating to multiple samples in order to correct the output noise of the measuring equipment for each lead and the corresponding electrocardiogram description information. For example, the computing device (100) can perform smoothing by specifying a window of a fixed sample length for the electrocardiogram description information generated for each lead and applying the mode of the resulting values ​​within the window to all windows, but the present disclosure is not limited thereto.

[0072] Figure 6 is a conceptual diagram showing several electrocardiogram characteristics that can be extracted from typical electrocardiogram description data. These electrocardiogram characteristics are information related to segmented regions that serve as indicators for identifying abnormalities in heart rhythm and the presence or absence of cardiac disease, and may include, but are not limited to, RR-interval, QRS duration (620), QT interval (650), or QT-corrected interval.

[0073] A computer-readable storage medium storing a data structure is disclosed based on one embodiment of the present disclosure.

[0074] A data structure can refer to the organization, management, and storage of data, enabling efficient access to and modification of that data. It can also refer to a data organization designed to solve a specific problem (e.g., data retrieval, data storage, data modification in the shortest possible time). A data structure can also be defined as the physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements can include the connections between data elements as perceived by the user. Physical relationships between data elements can include the actual relationships between data elements physically stored on a computer-readable storage medium (e.g., a hard disk). Specifically, a data structure can include a collection of data, relationships between data, and functions or commands that can be applied to data. A well-designed data structure allows computing devices to perform calculations while minimizing the use of their resources. Specifically, a computing device can improve the efficiency of operations such as arithmetic, reading, inserting, deleting, comparing, exchanging, and retrieving through a well-designed data structure.

[0075] Data structures can be classified into linear and nonlinear data structures based on their form. A linear data structure may be one in which only one data item is linked after another. Linear data structures can include lists, stacks, queues, and decks. A list can represent a series of datasets that have an internal order. A list can be a linked list. A linked list can be a data structure in which data is linked in a linear fashion, with each item having a pointer. In a linked list, pointers can contain linking information to the next or previous data item. Linked lists can be represented as single linked lists, double linked lists, or circular linked lists, depending on their form. A stack may be a data list structure in which data can be accessed restrictively. A stack may be a linear data structure in which data can only be processed (e.g., inserted or deleted) at one end of the data structure. Data stored in a stack may be a LIFO (Last In First Out) data structure, where data enters later and exits earlier. A queue is a data list structure that allows restrictive access to data, and unlike a stack, it can be a data structure where data stored later is retrieved later (FIFO - First In First Out). A deck can be a data structure where data can be processed at both ends of the data structure.

[0076] A nonlinear data structure can be a structure in which multiple data are concatenated after a single data. Nonlinear data structures can include graph data structures. Graph data structures can be defined by vertices and edges, and edges can include lines connecting two distinct vertices. Graph data structures can also include tree data structures. In a tree data structure, a path connecting two distinct vertices from among the multiple vertices contained in the tree can be a single data structure. In other words, it can be a data structure that does not form a loop in a graph data structure.

[0077] Throughout this specification, the terms computational model, neural network, network function, and neural network can be used interchangeably. (Hereafter, the term "neural network" will be used consistently.) A data structure may include a neural network. A data structure including a neural network may be stored on a computer-readable storage medium. A data structure including a neural network may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and a loss function for training the neural network. A data structure including a neural network may include any of the components of the configurations disclosed above. That is, a data structure including a neural network may consist of all or any combination thereof, such as data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and a loss function for training the neural network. In addition to the configurations described above, a data structure including a neural network may include any other information that determines the properties of the neural network. Furthermore, the data structure may include, and is not limited to, all forms of data used or generated during the computational process of the neural network. Computer-readable storage media may include computer-readable recording media and / or computer-readable transmission media. A neural network can consist of a set of interconnected computational units commonly called nodes. Such nodes may be called neurons. A neural network consists of at least one node.

[0078] A data structure may include data to be input to a neural network. A data structure containing data to be input to a neural network may be stored on a computer-readable storage medium. Data to be input to a neural network may include training data input during the training process of the neural network and / or input data to be input to a neural network after training is complete. Data to be input to a neural network may include data that has undergone preprocessing and / or data to be preprocessed. Preprocessing may include data processing processes to prepare data for input to a neural network. Therefore, a data structure may include data to be preprocessed and data generated during preprocessing. The data structures described above are illustrative examples, and this disclosure is not limited thereto.

[0079] The data structure may include the weights of a neural network. (The terms "weights" and "parameters" are used interchangeably herein.) The data structure containing the weights of a neural network may be stored on a computer-readable storage medium. A neural network may include multiple weights. The weights are variable and can be varied by the user or algorithm to enable the neural network to perform desired functions. For example, if one or more input nodes are interconnected to a single output node by their respective links, the output node's output node value can be determined based on the values ​​input to the input nodes connected to the output node and the parameters set on the links corresponding to each input node. The data structures described above are illustrative examples, and the disclosure is not limited thereto.

[0080] As an example rather than a limitation, weights may include weights that change during the neural network learning process and / or weights after neural network learning is complete. Weights that change during the neural network learning process may include weights at the start of a learning cycle and / or weights that change during a learning cycle. Weights after neural network learning is complete may include weights after a learning cycle is complete. Thus, a data structure containing neural network weights may include a data structure containing weights that change during the neural network learning process and / or weights after neural network learning is complete. Therefore, the weights and / or each combination of weights described above shall be included in the data structure containing neural network weights. The data structures described above are illustrative and the disclosure is not limited thereto.

[0081] A data structure containing the weights of a neural network can be stored on a computer-readable storage medium (e.g., memory, hard disk) after undergoing a serialization process. Serialization may be the process of converting a data structure into a form that can be stored on the same or other computing device and later reconfigured for use. A computing device can serialize a data structure and send and receive data over a network. A serialized data structure containing the weights of a neural network can be reconfigured on the same or other computing device through deserialization. A data structure containing the weights of a neural network is not limited to serialization. Furthermore, a data structure containing the weights of a neural network may include data structures designed to improve computational efficiency while minimizing the use of computing device resources (e.g., nonlinear data structures such as B-trees, tries, m-way search trees, AVL trees, Red-Black Trees). The foregoing is illustrative, and this disclosure is not limited thereto.

[0082] The data structure may include the hyperparameters of the neural network. The data structure containing the neural network's hyperparameters can be stored in a computer-readable storage medium. The hyperparameters may be variables that are variable by the user. Examples of hyperparameters include the learning rate, cost function, number of learning cycle iterations, weight initialization (e.g., setting the range of weights to be initialized), and number of Hidden Units (e.g., number of hidden layers, number of nodes in hidden layers). The aforementioned data structures are illustrative and the disclosure is not limited thereto.

[0083] Figure 7 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure can be realized.

[0084] While it has been stated that this disclosure can generally be embodied by computing devices, those skilled in the art will understand that this disclosure can also be embodied in combination with computer executable instructions and / or other program modules that can be run on one or more computers, and / or as a combination of hardware and software.

[0085] Generally, modules as defined herein include routines, programs, components, data structures, and so on, that perform a specific task or implement a specific abstract data type. Furthermore, those skilled in the art will understand that the methods disclosed herein can be implemented in configurations of other computer systems, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor boards, or programmable consumer electronics, and so on (each of which can operate in conjunction with one or more associated devices).

[0086] The embodiments described herein can further be implemented in a distributed computing environment in which a task is performed by remote processing units connected via a communication network. In a distributed computing environment, program modules can reside in both local and remote memory storage devices.

[0087] Computers include a variety of computer-readable media. Any media accessible by a computer can be computer-readable, but such computer-readable media include volatile and non-volatile media, transient and non-transitory media, and portable and non-portable media. By example, rather than by limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transient and non-transitory media, portable and non-portable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store information.

[0088] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of its characteristics have been set or modified to encode information within the signal. By example, rather than by limitation, computer-readable transmission media include wired media such as wired networks or direct-wired connections, and wireless media such as acoustic, RF, infrared, and other wireless media. Any combination of any of the aforementioned media is also included in the scope of computer-readable transmission media.

[0089] An exemplary environment (1100) is shown that realizes various aspects of this disclosure, including a computer (1102), the computer (1102) including a processor (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including (but not limited to) system memory (1106), to the processor (1104). The processor (1104) can be any processor from a variety of commercial processors. Dual processors and other multiprocessor architectures can also be used as the processor (1104).

[0090] The system bus (1108) can be one of several types of bus structures that can be further interconnected to a local bus using any of the following: a memory bus, a peripheral bus, and various commercial bus architectures. System memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). The basic input / output system (BIOS) is stored in non-volatile memory (1110) such as ROM, EPROM, or EEPROM, and this BIOS includes basic routines that support the exchange of information between multiple components within the computer (1102) during startup, etc. RAM (1112) may also include high-speed RAM such as static RAM for caching data.

[0091] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA)—this internal hard disk drive (1114) can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from and writing to a portable diskette (1118)), and an optical disk drive (1120) (e.g., for reading CD-ROM disks (1122) or for reading from and writing to other high-capacity optical media such as DVDs). The hard disk drive (1114), magnetic disk drive (1116), and optical disk drive (1120) can be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128), respectively. The interface (1124) for implementing external drives includes, for example, at least one or both of the following: USB (Universal Serial Bus) or IEEE 1394 interface technology.

[0092] These drives and computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and so on. In the case of a computer (1102), the drives and media correspond to storing any data in a suitable digital format. While the above description of computer-readable storage media refers to HDDs, portable magnetic disks, and portable optical media such as CDs or DVDs, those skilled in the art will understand that other types of computer-readable storage media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and so on, can also be used in exemplary operating environments, and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0093] Numerous program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), can be stored in the drive and RAM (1112). All or part of the operating system, applications, modules, and / or data can also be cached in RAM (1112). It will be understood that this disclosure can be implemented by various commercially available operating systems or combinations of operating systems.

[0094] The user can input commands and information to the computer (1102) through one or more wired or wireless input devices, such as a keyboard (1138) and a pointing device such as a mouse (1140). Other input devices (not shown in the diagram) may include a microphone, IR remote control, joystick, gamepad, stylus pen, touchscreen, and so on. These and other input devices are often connected to the processing unit (1104) through an input device interface (1142) connected to the system bus (1108), but they can also be connected through other interfaces such as parallel ports, IEEE1394 serial ports, game ports, USB ports, IR interfaces, and so on.

[0095] A monitor (1144) or other type of display device is also connected to the system bus (1108) through an interface such as a video adapter (1146). In addition to the monitor (1144), the computer generally includes other peripheral output devices such as speakers, printers, and so on (not shown in the illustration).

[0096] A computer (1102) can operate in a networked environment by utilizing logical connections to one or more remote computers (1148), such as multiple remote computers (1148), via wired and / or wireless communication. The multiple remote computers (1148) can be workstations, server computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other typical network nodes, and generally include many or all of the components described for a computer (1102), although for simplification only a memory storage device (1150) is illustrated. The illustrated logical connections include wired and wireless connections in a short-range network (LAN) (1152) and / or a larger network, such as a long-range network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies, facilitating enterprise-wide computer networks such as intranets, all of which can connect to global computer networks, such as the Internet.

[0097] When used in a LAN networking environment, the computer (1102) connects to the local network (1152) via a wired and / or wireless network interface, or via an adapter (1156). The adapter (1156) facilitates wired or wireless communication to the LAN (1152), which also includes a wireless access point installed therein to communicate with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), connect to a communication server on the WAN (1154), or have other means of establishing communication through the WAN (1154), such as via the Internet. The modem (1158), which can be internal or external, and wired or wireless, connects to the system bus (1108) via a serial port interface (1142). In a networked environment, a program module or part thereof described for a computer (1102) can be stored in a remote memory / storage device (1150). While the illustrated network connection is illustrative, it is readily apparent that other means of establishing communication links between multiple computers may be used.

[0098] The computer (1102) operates to communicate with any wireless device or unit that is arranged and operates wirelessly, such as a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or location relating to a wirelessly discoverable tag, and a telephone. This includes at least Wi-Fi and Bluetooth® wireless technologies. Thus, the communication may be a predefined structure like a conventional network, or simply ad hoc communication between at least two devices.

[0099] Wi-Fi (Wireless Fidelity) enables internet access and other connectivity without a wired connection. Wi-Fi is a wireless technology, similar to cell phones, that allows devices like computers to send and receive data indoors and outdoors, i.e., anywhere within the range of a base station. Wi-Fi networks use IEEE 802.11 (a, b, g, etc.) wireless technology to provide secure, reliable, and high-speed wireless connectivity. Wi-Fi can be used to connect computers to each other, to the internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands at data rates such as 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0100] A person with ordinary skill in the art of this disclosure will understand that information and signals can be represented using any variety of different techniques and methods. For example, the data, instructions, commands, information, signals, bits, symbols and chips referenced in the foregoing description can be represented by voltage, current, electromagnetic waves, magnetic fields, etc. or particles, optical fields, etc. or particles, or any combination thereof.

[0101] A person with ordinary skill in the art of this disclosure will understand that the various exemplary logic blocks, modules, processors, means, circuits, and algorithmic stages described herein can be implemented by electronic hardware, various forms of programs or design code (referred to herein for convenience as “software”), or a combination of all of these. To illustrate this interoperability of hardware and software, various exemplary components, blocks, modules, circuits, and stages have been generally described above with respect to their functions. Whether such functions are implemented in hardware or software depends on the design constraints imposed on a particular application and the overall system. A person with ordinary skill in the art of this disclosure can implement the functions described in various ways for individual specific applications, but such decisions should not be construed as departing from the scope of this disclosure.

[0102] The various embodiments described herein can be realized by methods, apparatus, or manufactured articles using standard programming and / or engineering techniques. The term “manufactured article” includes computer programs, carriers, or media accessible from any computer-readable device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). The various storage media described herein also include one or more devices and / or other machine-readable media for storing information.

[0103] It should be understood that the specific order or hierarchical structure of the multiple stages in the presented process is an example of an exemplary approach. It should be understood that, based on design priorities, the specific order or hierarchical structure of the stages in the process can be rearranged within the scope of this disclosure. The appended method claims provide a variety of stage elements in sample order, but are not limited to the specific order or hierarchical structure shown.

[0104] The descriptions relating to the embodiments provided herein are provided so that any person with ordinary skill in the art of the present disclosure may utilize or implement the present disclosure. Various variations of such embodiments are readily apparent to a person with ordinary skill in the art of the present disclosure, and the general principles defined herein can be applied to other embodiments without departing from the scope of the present disclosure. Accordingly, the present disclosure is not limited by the embodiments provided herein and should be interpreted in the broadest sense consistent with the principles and novel features provided herein.

Claims

1. A method performed by one or more processors of a computing device, The stage of receiving one or more electrocardiogram data from multiple leads; A step of generating electrocardiogram delineation (ECG delineation) information corresponding to multiple samples, each having a predetermined time length, from one or more received electrocardiogram data using an electrocardiogram delineation model; and The step of integrating the aforementioned multiple electrocardiogram description information; Includes, Here, the step of generating electrocardiogram description information corresponding to multiple samples, each having a predetermined time length, from one or more received electrocardiogram data using the electrocardiogram description model is: A step of generating the multiple samples by dividing one or more electrocardiogram data based on the predetermined length of time; and The step of generating electrocardiogram description information for each of the multiple samples using the electrocardiogram description model; The step of integrating the multiple electrocardiogram description information includes the step of generating a single electrocardiogram description information by integrating the electrocardiogram description information of the multiple samples. method.

2. In claim 1, The step of receiving the electrocardiogram data includes the step of receiving one or more electrocardiogram data from one of several electrocardiogram measurement devices of different types. method.

3. In claim 1, The aforementioned preset time length is determined independently of the number of leads of the electrocardiogram measurement equipment or the length of the measurement time. method.

4. In claim 1, The aforementioned electrocardiogram description information is: P-wave location information Location information of a group of QRS signals (QRS-complex); or T-wave position information; It includes at least one of the following, The processor uses the electrocardiogram description information to P-wave amplitude information; Amplitude information of the QRS group; or T-wave amplitude information Derive at least one of the following: method.

5. In claim 1, The steps for generating the single electrocardiogram description information are as follows: A step of generating smoothed electrocardiogram description information relating to the plurality of samples; and A step of integrating the smoothed electrocardiogram description information relating to the aforementioned multiple samples; including, method.

6. In claim 5, The step of generating smoothed electrocardiogram description information relating to the aforementioned multiple samples is: The step of deriving a representative value from among multiple result values ​​of electrocardiogram description information relating to the multiple samples within a fixed time interval. Further including, method.

7. In claim 5, The step of integrating the smoothed electrocardiogram description information relating to the aforementioned multiple samples is: The step of deriving a representative value from among the multiple result values ​​of the smoothed electrocardiogram description information. Further including, method.

8. In claim 1, The aforementioned method, The step of extracting electrocardiogram characteristics from the integrated electrocardiogram description information. It also includes, The step of extracting electrocardiogram characteristics from the aforementioned integrated electrocardiogram description information is as follows: The step of dividing the integrated electrocardiogram description information based on the duration of a single heartbeat; The step of extracting each electrocardiogram characteristic from the electrocardiogram description information divided into time intervals for each heartbeat; and The stage of deriving representative values ​​for each extracted electrocardiogram characteristic; including, method.

9. In claim 8, The aforementioned electrocardiogram characteristics are, RR-interval; QRS duration; QT interval; or QT - Corrected Interval; at least one of the following: method.

10. In claim 1, The aforementioned method, A step of generating a user interface (UI) that includes information related to the electrocardiogram description information; It further includes, The aforementioned user interface is: The first area displays electrocardiogram data measured by the electrocardiogram measurement equipment; A second area for displaying the electrocardiogram description information; and The third area displays electrocardiogram characteristic values; including, method.

11. A computer program stored on a computer-readable storage medium, which includes instructions causing a computing device to perform an action, wherein the action is: An operation to receive one or more electrocardiogram data from the aforementioned multiple leads; An operation to generate multiple electrocardiogram description information corresponding to multiple samples, each having a predetermined time length, from one or more received electrocardiogram data using an electrocardiogram description model; and An operation to integrate the aforementioned multiple electrocardiogram description information; Includes, Here, the step of generating electrocardiogram description information corresponding to multiple samples, each having a predetermined time length, from one or more received electrocardiogram data using the electrocardiogram description model is: A step of generating the multiple samples by dividing one or more electrocardiogram data based on the predetermined length of time; and The step of generating electrocardiogram description information for each of the multiple samples using the electrocardiogram description model; The operation of integrating the multiple electrocardiogram description information includes the operation of generating a single electrocardiogram description information by integrating the electrocardiogram description information of the multiple samples. A computer program stored on a computer-readable storage medium.

12. A computing device, A processor containing one or more cores; A network unit that receives one or more electrocardiogram data; and memory; Includes, The aforementioned processor, Receive one or more electrocardiogram data from multiple leads, Using an electrocardiogram description model, multiple electrocardiogram description pieces are generated from one or more received electrocardiogram data, each corresponding to multiple samples with a predetermined time length, and The aforementioned multiple electrocardiogram description information is integrated, Here, using the electrocardiogram description model, generating electrocardiogram description information corresponding to multiple samples, each having a predetermined time length, from one or more received electrocardiogram data points is: To generate the multiple samples by dividing one or more electrocardiogram data based on the predetermined time length; and This includes generating electrocardiogram description information for each of the multiple samples using the electrocardiogram description model; Integrating the aforementioned multiple electrocardiogram description information includes generating a single electrocardiogram description information by integrating the electrocardiogram description information of the aforementioned multiple samples. Computing device.

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