KR20240009349A - Method, program and apparatus for providing visualization contents based on electrocardiogram interpreting - Google Patents

Abstract

The present disclosure relates to a method, program, and device for providing visualization content based on electrocardiogram readings, the method comprising: providing visualization content based on electrocardiogram readings, performed by a computing device including at least one processor; acquiring; Analyzing the ECG data to generate ECG reading data; And based on the generated ECG reading data, generating visualization content including an ECG graph representing an ECG waveform of the ECG data and heart animation data visualizing an anatomical heart, wherein the heart animation data includes, An object is to provide a method that is played in synchronization with the electrocardiogram graph and can display heart condition information including at least one of heart movement, blood flow, or electric flow.

Description

Method, program, and device for providing visualization content based on electrocardiogram reading {METHOD, PROGRAM AND APPARATUS FOR PROVIDING VISUALIZATION CONTENTS BASED ON ELECTROCARDIOGRAM INTERPRETING}

The present disclosure relates to a method of providing visualization content based on electrocardiogram reading. Specifically, it is possible to provide visualization content about the electrocardiogram waveform and the anatomical appearance of the heart based on the electrocardiogram reading data obtained by analyzing the electrocardiogram data. It's about how to be.

An electrocardiogram (ECG) is a signal that measures electrical signals generated in the heart and checks for abnormalities in the conduction system from the heart to the electrodes to determine the presence or absence of disease.

The heartbeat, which is the cause of the electrocardiogram, is an impulse that originates from the sinus node located in the right atrium, first depolarizes the right and left atrium, and after a brief delay in the atrioventricular node, Activates the ventricles.

The right ventricle, which has the fastest septum and thin walls, activates before the left ventricle, which has thick walls. The depolarization wave transmitted to the Purkinje fibers spreads from the endocardium to the epicardium like a wavefront in the myocardium, causing ventricular contraction. Because electrical impulses are normally conducted through the heart, the heart contracts approximately 60 to 100 times per minute. Each contraction is represented by one heart beat.

Such an electrocardiogram can be detected through a bipolar lead, which records the potential difference between two parts, and a unipolar lead, which records the potential of the area where the electrode is attached. Methods for measuring an electrocardiogram include the bipolar lead. There is a standard limb lead, a unipolar limb lead, and a unipolar thoracic lead (precordial lead).

The electrical activity stage of the heart is largely divided into atrial depolarization, ventricular depolarization, and ventricular repolarization, and each of these stages is reflected in the form of several waves called P, Q, R, S, and T waves, as shown in Figure 1.

These waves must have a standard shape for the heart's electrical activity to be considered normal. In order to determine whether it is a standard shape or not, it is necessary to check whether characteristics such as the time each wave is maintained, the interval between each wave, the amplitude of each wave, and kurtosis are within the normal range (or standard).

These electrocardiograms are measured with expensive measuring equipment and used as an auxiliary tool to measure the patient's health status. In general, electrocardiogram measuring equipment only displays measurement results and diagnosis is entirely up to the doctor.

In particular, the 24-hour electrocardiogram test is a test that measures electrocardiogram changes after a small cassette-sized measuring device is mounted on the user's body and the electrocardiogram test is completed after a certain period of time (for example, about 20 hours). This 24-hour electrocardiogram test is a test to diagnose heart disease by checking on the electrocardiogram whether symptoms such as dizziness, fainting, palpitations, and chest pain that appear during daily life are related to arrhythmia. However, during the test, the device must be attached and There is an inconvenience in that the user has to visit the examination room (e.g., hospital, etc.) twice to remove the device. In reality, it is difficult to visit the hospital multiple times to detect intermittently abnormal heart signals through electrocardiography, and patients with mild symptoms or receiving follow-up care due to past diseases spend a lot of time and effort on visiting the hospital. There is a problem that you have to devote time to it.

Currently, research is ongoing to quickly and accurately diagnose diseases using artificial intelligence based on electrocardiograms to reduce dependence on doctors. In addition, with the development of wearable self-electrocardiogram measurement devices such as smart watches, the possibility of diagnosing and monitoring not only heart disease but also various other diseases based on electrocardiogram is emerging.

Therefore, future ECG testing systems are not limited to users continuously measuring ECG in their daily lives, but can quickly and accurately identify the user's ECG data through a pre-trained neural network model by linking it with the medical information system installed in the hospital. In addition, we must be able to provide a platform that allows ordinary people without medical knowledge to easily understand ECG signals.

Republic of Korea Patent Publication No. 10-2014-0037326 (2014.03.27)

The present disclosure was made in response to the above-mentioned background technology, and presents heart condition information, including heart movement, blood flow, and electrical flow, along with ECG waveforms in the form of an anatomical heart so that users without medical knowledge can understand ECG data. The purpose is to provide a method of providing visualization content based on ECG reading that can be displayed in a visualized animation form and, in the case of a heart disease, can be intuitively displayed by visualizing the ECG waveform and heart shape related to the disease.

However, the problems to be solved by this disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood based on the description below.

In order to achieve the above-mentioned problem, the present disclosure seeks to provide a method of providing visualization content based on electrocardiogram reading. The method includes providing visualization content based on electrocardiogram readings, performed by a computing device including at least one processor, the method comprising: acquiring electrocardiogram data; Analyzing the ECG data to generate ECG reading data; And based on the generated ECG reading data, generating visualization content including an ECG graph representing an ECG waveform from the ECG data and heart animation data visualizing the anatomical appearance of the heart, wherein the heart animation data includes, It is played in synchronization with the electrocardiogram graph and displays heart condition information including at least one of heart movement, blood flow, or electrical flow.

Alternatively, the electrocardiogram reading data may include classification information related to heart disease, which is calculated by a first model included in a pre-trained neural network model.

Alternatively, analyzing the ECG data to generate ECG reading data may include determining, based on the classification information, whether one or more of a plurality of heart diseases exists; And when it is determined that one or more of the plurality of heart diseases exists through the classification information, extracting at least one waveform information corresponding to the heart disease determined to exist from a pre-built database. there is.

Alternatively, analyzing the ECG data to generate ECG reading data may include extracting ECG features related to waveforms from the ECG data.

Alternatively, the electrocardiogram features may include P waves, QRS complex, and T waves.

Alternatively, the step of generating visualization content including an ECG graph representing an ECG waveform of the ECG data and heart animation data visualizing an anatomical heart based on the generated ECG reading data includes: It may include generating the ECG graph by visualizing the waveform corresponding to the classification information and the waveform information so that at least one of color or shape is distinguishable from the remaining waveforms.

Alternatively, the step of generating visualization content including an ECG graph representing an ECG waveform of the ECG data and heart animation data visualizing an anatomical heart based on the generated ECG reading data includes: In a waveform, synchronized with the classification information and a waveform corresponding to the waveform information, visually highlighting an anatomical heart shape corresponding to the heart disease determined to exist, thereby generating the heart animation data. You can.

Alternatively,

The step of generating visualization content including an ECG graph representing an ECG waveform of the ECG data and heart animation data visualizing an anatomical heart based on the generated ECG reading data includes the ECG features extracted from the ECG data. Calculating the heart axis based on and generating the heart animation data by arranging the anatomical shape of the heart based on the calculated heart axis.

Alternatively, the step of calculating a heart axis based on the ECG features extracted from the ECG data may include calculating a standard lead and a limb lead in the ECG data based on the ECG features, respectively. calculating the net amplitude of; And it may include calculating the angle of the heart axis based on a mathematical operation using the summed amplitude of the standard leads and the summed amplitude of the limb leads as input variables.

Alternatively, the step of calculating a heart axis based on the extracted electrocardiogram features from the electrocardiogram data may be performed when the calculated heart axis is not included in the range of 45 degrees to 90 degrees based on lead I. , It may include generating the heart animation data by arranging the anatomical shape of the heart based on 60 degrees.

Alternatively, the step of providing a user interface for playing the visualization content to the user terminal may be further included.

Alternatively, the step of providing a user interface for reproducing the visualization content to a user terminal may include, when a first event occurs based on a user input for a reference point or baseline preset in the electrocardiogram graph, the anatomy corresponding to the reference point or baseline. It may include reproducing the visualization content so that an image of an enemy heart is displayed.

Alternatively, the step of providing a user interface for reproducing the visualization content to the user terminal includes: It may include reproducing the visualization content so that at least one of color, brightness, saturation, or highlight effect of the heart shape changes.

Alternatively, the step of providing a user interface for playing the visualization content to a user terminal may include a user specifying a waveform corresponding to the classification information and the waveform information in the ECG graph once while the visualization content is stopped. When a second event based on the input occurs, the method may include reproducing the visualization content so that a normal ECG waveform without a disease is displayed.

Alternatively, the step of providing a user interface for playing the visualization content to a user terminal may include displaying a waveform corresponding to the classification information and the waveform information in the ECG graph for a predetermined period while the visualization content is stopped. When a third event based on a specific user input occurs, the method may include reproducing the visualization content so that an electrocardiogram waveform showing a disease corresponding to the classification information is displayed.

A computer program stored in a computer-readable storage medium according to another embodiment of the present disclosure, wherein the computer program, when executed on one or more processors, performs operations for providing visualization content based on electrocardiogram reading. And the operations include: acquiring electrocardiogram data; Analyzing the electrocardiogram data to generate electrocardiogram reading data; And an operation of generating visualization content including an electrocardiogram graph representing an electrocardiogram waveform of the electrocardiogram data based on the generated electrocardiogram reading data and cardiac animation data visualizing an anatomical shape of the heart, wherein the cardiac animation data includes, It is played in synchronization with the electrocardiogram graph and displays heart condition information including at least one of heart movement, blood flow, or electrical flow.

Meanwhile, according to an embodiment of the present disclosure, a computing device for providing visualization content based on electrocardiogram readings includes: a processor including at least one core; and a memory including program codes executable by the processor, wherein the processor acquires electrocardiogram data according to execution of the program code, analyzes the electrocardiogram data, and provides electrocardiogram reading data. Generates visualization content including an electrocardiogram graph representing the electrocardiogram waveform of the electrocardiogram data and cardiac animation data visualizing the anatomical appearance of the heart based on the generated electrocardiogram reading data, wherein the cardiac animation data includes the electrocardiogram data, It is played in synchronization with the graph and displays heart condition information including at least one of heart movement, blood flow, or electric flow.

A method of providing visualization content based on electrocardiogram reading according to an embodiment of the present disclosure may provide visualization content about the anatomical appearance of the heart along with the electrocardiogram waveform based on electrocardiogram reading data obtained by analyzing electrocardiogram data. Also, in the case of a heart disease, the electrocardiogram waveform and heart shape corresponding to the disease can be intuitively displayed, so users without medical knowledge can easily increase their understanding of the electrocardiogram, as well as provide information to patients with heart disease. It has the effect of increasing your understanding of your disease, which can be helpful in active treatment.

At this time, the present disclosure is synchronized with the electrocardiogram waveform to express heart movement, blood flow, or electric flow in the anatomical shape of the heart and display it in an animated manner, so that anyone can easily and interestingly understand the electrocardiogram, and have interest in cardiac activity and heart health. This has the effect of increasing it.

1 is a diagram showing electrocardiogram data according to the present disclosure.
2 is a block diagram of a computing device according to an embodiment of the present disclosure.
Figure 3 is a block diagram illustrating the configuration of a system that provides visualization content based on electrocardiogram readings according to an embodiment of the present disclosure.
Figure 4 is an example diagram explaining visualization content according to an embodiment of the present disclosure.
FIG. 5 is a diagram illustrating electrocardiogram graphs measured in standard leads and limb leads according to an embodiment of the present disclosure.
FIG. 6 is a diagram illustrating electrode positions and the heart axis in standard ECG guidance according to an embodiment of the present disclosure.
Figure 7 is an example diagram illustrating the summed amplitude of QRS waveforms according to an embodiment of the present disclosure.
Figure 8 is a flowchart explaining a method of providing visualization content based on electrocardiogram reading according to an embodiment of the present disclosure.
Figure 9 is an example diagram illustrating a synchronization process between an electrocardiogram graph and heart animation data according to an embodiment of the present disclosure.
FIG. 10 is an example diagram illustrating heart animation data in which the electrocardiogram and the electrical flow of the heart are synchronized according to an embodiment of the present disclosure.
FIG. 11 is an exemplary diagram illustrating an electrocardiogram waveform correlated with a diagnosis result of heart disease according to an embodiment of the present disclosure.
FIG. 12 is an exemplary diagram illustrating a normal ECG waveform (a) and an abnormal ECG waveform (b) according to an embodiment of the present disclosure.
Figure 13 is an example diagram illustrating an electrocardiogram graph in which electrocardiogram waveforms extracted based on the diagnosis results of heart disease are differentially displayed according to an embodiment of the present disclosure.
Figure 14 is a flowchart explaining a method of playing visualization content according to an embodiment of the present disclosure.
Figure 15 is an exemplary diagram showing an electrocardiogram graph according to an embodiment of the present disclosure.
FIG. 16 is a diagram illustrating a process of synchronizing and playing back an electrocardiogram graph and heart animation data according to an embodiment of the present disclosure.
FIG. 17 is a diagram illustrating a normal standard for an ECG waveform based on ECG characteristics according to an embodiment of the present disclosure.

Below, with reference to the attached drawings, embodiments of the present disclosure are described in detail so that those skilled in the art (hereinafter referred to as skilled in the art) can easily implement the present disclosure. The embodiments presented in this disclosure are provided to enable any person skilled in the art to use or practice the subject matter of this disclosure. Accordingly, various modifications to the embodiments of the present disclosure will be apparent to those skilled in the art. That is, the present disclosure can be implemented in various different forms and is not limited to the following embodiments.

The same or similar reference numerals refer to the same or similar elements throughout the specification of this disclosure. Additionally, in order to clearly describe the present disclosure, reference numerals in the drawings may be omitted for parts that are not related to the description of the present disclosure.

As used in this disclosure, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “x uses a or b” should be understood to mean one of natural implicit substitutions. For example, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “x uses a or b” means that x uses a, x uses b, or x uses a and It can be interpreted as one of the cases where both b are used.

The term “and/or” as used in this disclosure should be understood to refer to and include all possible combinations of one or more of the listed related concepts.

The terms “comprise” and/or “comprising” as used in this disclosure should be understood to mean that certain features and/or elements are present. However, the terms "comprise" and/or "including" should be understood as not excluding the presence or addition of one or more other features, other components, and/or combinations thereof.

Unless otherwise specified in this disclosure or the context is clear to indicate a singular form, the singular should generally be construed to include “one or more.”

The term "th nth (n is a natural number)" used in the present disclosure can be understood as an expression used to distinguish the components of the present disclosure according to a predetermined standard such as a functional perspective, a structural perspective, or explanatory convenience. there is. For example, in the present disclosure, components performing different functional roles may be distinguished as first components or second components. However, components that are substantially the same within the technical spirit of the present disclosure but must be distinguished for convenience of explanation may also be distinguished as first components or second components.

The term “acquisition” used in this disclosure is understood to mean not only receiving data through a wired or wireless communication network with an external device or system, but also generating data in an on-device form. It can be.

Meanwhile, the term "module" or "unit" used in this disclosure refers to a computer-related entity, firmware, software or part thereof, hardware or part thereof. , can be understood as a term referring to an independent functional unit that processes computing resources, such as a combination of software and hardware. At this time, the “module” or “unit” may be a unit composed of a single element, or may be a unit expressed as a combination or set of multiple elements. For example, a "module" or "part" in the narrow sense is a hardware element or set of components of a computing device, an application program that performs a specific function of software, a process implemented through the execution of software, or a program. It can refer to a set of instructions for execution, etc. Additionally, as a broad concept, “module” or “unit” may refer to the computing device itself constituting the system, or an application running on the computing device. However, since the above-described concept is only an example, the concept of “module” or “unit” may be defined in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

As used in this disclosure, the term "model" refers to a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or a process to solve a specific problem. It can be understood as an abstract model of a process. For example, a neural network “model” may refer to an overall system implemented as a neural network that has problem-solving capabilities through learning. At this time, the neural network can have problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning. A neural network “model” may include a single neural network or a neural network set in which multiple neural networks are combined.

The term “block” used in the present disclosure can be understood as a set of components divided based on various criteria such as type, function, etc. Accordingly, the configuration classified as one “block” can be changed in various ways depending on the standard. For example, a neural network “block” can be understood as a set of neural networks containing at least one neural network. At this time, it can be assumed that the neural networks included in the neural network “block” perform the same specific operation. The explanation of the foregoing terms is intended to aid understanding of the present disclosure. Therefore, if the above-mentioned terms are not explicitly described as limiting the content of the present disclosure, it should be noted that the content of the present disclosure is not used in the sense of limiting the technical idea.

Figure 2 is a block diagram of a computing device according to an embodiment of the present disclosure.

The computing device 100 according to an embodiment of the present disclosure may be a hardware device or part of a hardware device that performs comprehensive processing and calculation of data, or may be a software-based computing environment connected to a communication network. For example, the computing device 100 may be a server that performs intensive data processing functions and shares resources, or it may be a client that shares resources through interaction with the server. Additionally, the computing device 100 may be a cloud system in which a plurality of servers and clients interact to comprehensively process data. Since the above description is only an example related to the type of computing device 100, the type of computing device 100 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

Referring to FIG. 2, the computing device 100 according to an embodiment of the present disclosure may include a processor 110, a memory 120, and a network unit 130. there is. However, since FIG. 2 is only an example, the computing device 100 may include other components for implementing a computing environment. Additionally, only some of the configurations disclosed above may be included in computing device 100.

The processor 110 according to an embodiment of the present disclosure may be understood as a structural unit including hardware and/or software for performing computing operations. For example, the processor 110 may read a computer program and perform data processing for machine learning. The processor 110 may process computational processes such as processing input data for machine learning, extracting features for machine learning, and calculating errors based on backpropagation. The processor 110 for performing such data processing includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and a custom processing unit (TPU). It may include a semiconductor (ASICc: application specific integrated circuit), or a field programmable gate array (FPGA: field programmable gate array). Since the type of processor 110 described above is only an example, the type of processor 110 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

The processor 110 uses a first model to identify the presence of disease based on electrocardiogram data and a second model to extract electrocardiogram features including P wave, QRS complex, and T wave from electrocardiogram data. A neural network model that diagnoses can be trained. For example, the processor 110 may learn a neural network model to estimate heart disease by analyzing the ECG based on biological information including information such as gender, age, weight, height, etc., along with the ECG data. Specifically, the processor 110 may input electrocardiogram data and various biological information into the neural network model and train the neural network model to detect changes in the electrocardiogram due to arrhythmia or other heart diseases. At this time, the neural network model can perform learning based on an ECG dataset that includes ECG features extracted from ECG data and diagnostic data for arrhythmia and other heart diseases. The processor 110 may perform an operation representing at least one neural network block included in the neural network model during the learning process of the neural network model.

The processor 110 extracts ECG features, classification information, and waveform information from ECG data obtained from an ECG meter using a neural network model generated through the above-described learning process, and estimates ECG reading data based on the extracted information. You can. Here, the ECG reading data includes classification information related to heart disease, waveform information extracted from a database pre-built based on the classification information, and ECG characteristic information including P wave, QRS complex, and T wave extracted from the user's ECG data. It can be included.

The processor 110 inputs biological information including electrocardiogram data and information such as gender, age, weight, height, etc. into a neural network model learned through the above-described process to generate inference data representing the result of estimating the probability of heart disease. can be created. For example, the processor 110 inputs ECG data into a trained neural network model and can predict ECG reading data such as the presence or progress of arrhythmia or other heart disease.

In addition to the examples described above, the types of medical data and the output of the neural network model may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

Additionally, the processor 110 may generate visualization content that can show ECG waveforms, heart movements, blood flow, or electricity flow based on ECG reading data analyzed through a neural network model. At this time, the visualization content may include an ECG graph representing an ECG waveform in ECG data, and heart animation data that is synchronized with the ECG graph and visually shows the anatomical appearance of the heart.

The memory 120 according to an embodiment of the present disclosure may be understood as a structural unit including hardware and/or software for storing and managing data processed in the computing device 100. That is, the memory 120 can store any type of data generated or determined by the processor 110 and any type of data received by the network unit 130. For example, the memory 120 may be a flash memory type, hard disk type, multimedia card micro type, card type memory, or random access memory (RAM). ), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), PROM (prom: programmable read-only memory), magnetic memory , may include at least one type of storage medium among a magnetic disk and an optical disk. Additionally, the memory 120 may include a database system that controls and manages data in a predetermined system. Since the type of memory 120 described above is only an example, the type of memory 120 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.

The memory 120 can structure, organize, and manage data necessary for the processor 110 to perform operations, combinations of data, and program codes executable on the processor 110. For example, the memory 120 may store ECG data received through the network unit 130, which will be described later. The memory 120 includes program code that operates the neural network model to receive medical data and perform learning, program code that operates the neural network model to receive medical data and perform inference according to the purpose of use of the computing device 100, and Processed data generated as the program code is executed can be saved.

The network unit 130 according to an embodiment of the present disclosure may be understood as a structural unit that transmits and receives data through any type of known wired or wireless communication system. For example, the network unit 130 may be connected to a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), or wireless (WIBRO). broadband internet, 5th generation mobile communication (5g), ultra wide-band wireless communication, zigbee, radio frequency (RF) communication, wireless LAN, wireless fidelity ), data transmission and reception can be performed using a wired or wireless communication system such as near field communication (NFC), or Bluetooth. Since the above-described communication systems are only examples, the wired and wireless communication systems for data transmission and reception of the network unit 130 may be applied in various ways other than the above-described examples.

The network unit 130 may receive data necessary for the processor 110 to perform calculations through wired or wireless communication with any system or client. Additionally, the network unit 130 may transmit data generated through the calculation of the processor 110 through wired or wireless communication with any system or any client. For example, the network unit 130 may receive medical data through communication with an electrocardiogram monitor 10, including a wearable device. The network unit 130 may transmit output data of the neural network model, intermediate data derived from the calculation process of the processor 110, processed data, etc. through communication with the above-mentioned devices.

FIG. 3 is a block diagram illustrating the configuration of a system that provides visualization content based on electrocardiogram reading according to an embodiment of the present disclosure, and FIG. 4 is an example diagram explaining visualization content according to an embodiment of the present disclosure.

A system that provides visualization content based on electrocardiogram readings includes, but is not limited to, at least one electrocardiogram measuring device 10 and a computing device 100.

The electrocardiogram measuring device 10 can use various devices capable of measuring electrocardiograms, such as wearable devices that are worn on the user's body and can measure and collect various health indicators such as heart rate, body fat percentage, and blood pressure, and electrocardiogram kiosks. At this time, the electrocardiogram measuring device 10 can measure the electrocardiogram using various electrode combinations, such as a 12-guide method and a 6-guide method, as well as a single-guide method using a wearable device such as a wrist watch or a patch. It is desirable that the electrocardiogram measurement time is also set by adding or subtracting depending on the signal to be obtained.

This electrocardiogram measuring device 10 may include a user terminal connected to a wearable device, including an electronic appcessory and a smartwatch, which have received medical device approval from the Ministry of Food and Drug Safety to measure the electrocardiogram. there is.

The computing device 100 uses the pre-trained neural network model 200 to extract ECG features including P waves, QRS complexes, and T waves based on ECG data acquired from the ECG meter 10. Based on the ECG data, ECG reading data including diagnostic data such as the presence or absence of arrhythmia or other heart disease and the degree of progression can be generated. At this time, the neural network model may be learned based on ECG characteristics including at least one of the frequency of tachycardia, the length of the QT interval, the direction of deviation of the P wave, R wave, and T wave, or the QRS duration.

The computing device 100 provides an electrocardiogram (ECG) including a P wave, QRS complex, and T wave based on a first model that determines whether one or more of a plurality of heart diseases exists in a pre-trained neural network model and ECG data. It may include a second model that extracts feature information. Meanwhile, the computing device 100 may use the second model, but in addition to the second model, ECG features may be extracted using machine learning or various statistical techniques.

Accordingly, the computing device 100 provides classification information related to heart disease using a pre-trained first model, and when it is determined that one or more of the plurality of heart diseases exists, the computing device 100 provides classification information related to the heart disease determined to exist. At least one corresponding waveform information can be extracted from a pre-built database.

In addition, as shown in FIG. 4, the computing device 100 provides visualization content including an electrocardiogram graph 310 representing an electrocardiogram waveform based on electrocardiogram reading data and heart animation data 320 visualizing the anatomical appearance of the heart. creates . At this time, the heart animation data 320 may represent heart condition information including at least one of heart movement, blood flow, or electric flow through various color expressions and image expressions such as two-dimensional images, three-dimensional images, or real-life images.

The computing device 100 synchronizes the electrocardiogram graph 310 and the heart animation data 320 so that the heart animation data 32 is played. At this time, the computing device 100 extracts the ECG graph 310 representing the ECG waveform from the ECG data using a pre-trained neural network model, and uses at least one of the start point, end point, or duration of the ECG waveform based on the ECG characteristics. After determining, it is possible to synchronize the heart movement, blood flow, or electrical flow in the ECG waveform and the anatomical view of the heart based on at least one of the start point, end point, or duration of the ECG waveform.

This computing device 100 may provide a user interface capable of transmitting ECG data and playing visualization content to a user terminal. Accordingly, the user can transmit the electrocardiogram data measured by the wearable device to the computing device 100 through the user terminal, and play visualization content based on the electrocardiogram reading data to provide the user's heart condition information (cardiac activity, heart health, etc.) Can be understood without medical help.

The computing device 100 can calculate the cardiac axis (Ha) for each individual based on electrocardiogram data and electrocardiogram characteristics, and arranges the anatomical shape of the heart in the cardiac animation data 320 based on the calculated cardiac axis (Ha). can do.

FIG. 5 is a diagram illustrating electrocardiogram graphs measured in standard and limb leads according to an embodiment of the present disclosure, and FIG. 6 is a diagram illustrating electrode positions and heart axes in standard electrocardiogram induction according to an embodiment of the present disclosure. 7 is an example diagram illustrating the summed amplitude of the QRS waveform according to an embodiment of the present disclosure.

The computing device 100 calculates the heart axis (Ha) using the ECG features of the P wave, QRS complex, and T wave of the ECG graph, based on ECG features extracted using a pre-trained neural network model.

The standard 12-lead electrocardiogram records the standard leads, limb leads, and precordial leads. In particular, the standard leads and limb leads record the electrocardiogram in the front part of the heart, and the thoracic leads record the electrocardiogram in the horizontal part of the heart. do.

As shown in Figures 5 and 6, there are three standard limb leads (I, II, and III) that record the potential difference between the subject's left and right hands, the potential difference between the right hand and left foot, and the potential difference between the left foot and right hand. , There are three amplified unipolar limb leads (aVR, aVL, aVF) that amplify and record the potentials of the right hand, left hand, and left foot, respectively.

The normal standards for the heart axis (Ha) using the QRS waveform vary, but in general, 0° to +90° is a normal heart axis, 0° to -90° is left axis deviation, +90° to +180° is right axis deviation, and -90° to -180° is called severe axis deviation. The cardiac axis (Ha) using the waveform of the QRS complex is usually divided into four zones by lead I and lead aVF. If the sum of the QRS complex in both leads is upward, it is a normal electric axis, and if it is upward in lead I and lead aVF If it is downward, it is left axis deviation. Also, if it is downward in lead I and upward in lead aVF, it can be said to be right axis deviation, and if it is downward in both leads, it can be said to be severe axis deviation.

The computing device 100 calculates the net amplitude of each of the standard leads and limb leads from the ECG data based on the ECG characteristics, and uses the summed amplitude of the standard leads and the summed amplitude of the limb leads as input variables. Based on Equation 1, the angle of the heart axis (Ha) can be calculated. For example, the computing device 100 may calculate the angle of the heart axis using the summed amplitude of induced I and induced aVF based on Equation 1 below. At this time, the summed amplitude may include a summed QRS amplitude (net QRS amplitude), a summed P amplitude (net P amplitude), and a summed T amplitude (net T amplitude).

[Equation 1]

Here, the summed QRS amplitude is the same value as QRS balance and means whether the QRS direction is positive or negative, and the summed QRS amplitude is the positive number of the highest point in the positive direction. It is calculated by adding the value and the negative value of the lowest point in the negative direction. For example, as shown in Figure 7, in the QRS complex, when the amplitude of the R wave = +5, the amplitude of the Q wave = -1, and the amplitude of the S wave = -3, the summed QRS amplitude is 5+ (-3)=2.

At this time, the QRS complex is formed by ventricular depolarization and consists of three waves. 12 In a standard lead electrocardiogram, the QRS complex can be used to evaluate heart rate, cardiac electrical axis, and degree of rotation, as well as the presence of intraventricular conduction abnormalities.

The summed P amplitude and summed T amplitude may mean the highest point in the positive direction of the P wave and T wave, respectively. If there is a lowest point in the negative direction, such as P'/T', the summed P amplitude and the summed T amplitude have a positive value at the highest point in the positive direction and a negative value at the lowest point in the negative direction, respectively. It can be calculated by adding up.

On the other hand, if the summed P amplitude (net P amplitude) and the summed T amplitude (net T amplitude) cannot be obtained, the computing device 100 calculates the summed amplitude using the derived aVL and derived II pair, and adds 30 to the value. By adding up the degrees, you can calculate the angle of the heart axis. If the above-described value cannot be obtained, the computing device 100 calculates the summed amplitude using a pair of derived aVR and derived III, and the derived aVR may use a value multiplied by -1. Additionally, the computing device 100 may obtain the angle of the heart axis by adding 30 degrees to the summed amplitude value obtained using the pair of induction aVR and induction III.

The computing device 100 may generate heart animation data by arranging the anatomical shape of the heart based on the angle of the heart axis calculated in this way. Since people generally do not know that the axis of the heart is different for each person, showing the heart based on the axis of the heart has the advantage of adding interest and making it easier to identify a personalized heart shape. In addition, in the case of normal hearts, the angle of the heart axis is mostly within the range of 0 degrees to 90 degrees, so there is also the advantage that the user can easily predict the possibility of disease based on the appearance of the heart arranged with respect to the heart axis. . However, if the shape of the heart is arranged according to the angle of the heart axis, it may be difficult to intuitively understand the shape of the heart itself, so the computing device 100 sets the calculated angle of the heart axis to 45 to 90 degrees based on induction I. If it is not included in the category, heart animation data can be generated by placing the anatomical shape of the heart at 60 degrees.

FIG. 8 is a flowchart illustrating a method of providing visualization content based on ECG reading according to an embodiment of the present disclosure, and FIG. 9 is a flowchart illustrating a synchronization process of an ECG graph and heart animation data according to an embodiment of the present disclosure. This is an exemplary diagram, and FIG. 10 is an exemplary diagram illustrating heart animation data in which the electrocardiogram and the electrical flow of the heart are synchronized according to an embodiment of the present disclosure.

Referring to FIG. 8, the computing device 100 may acquire ECG data from at least one ECG meter 10 (S10). At this time, the ECG data may include biological information along with the ECG for each user.

The computing device 100 analyzes ECG data using a pre-trained neural network model, extracts ECG features based on user-specific ECG data using machine learning or various statistical techniques, and determines whether one or more of a plurality of heart diseases exists. Based on the judgment, classification information can be provided (S20). The electrocardiogram is a unique signal for each individual because it varies depending on gender, age, heart location, and size.

Accordingly, the computing device 100 estimates ECG reading data for the user using biological information including at least one of age, gender, weight, and height, along with the extracted ECG characteristics and classification information (S30). At this time, when it is determined that one or more of the plurality of heart diseases exists through the classification information, the computing device 100 extracts at least one waveform information corresponding to the heart disease determined to exist from a pre-built database. .

Here, the neural network model uses a deep learning algorithm to learn electrocardiogram data for each characteristic information of the electrocardiogram, and uses the learned model to derive electrocardiogram reading data including classification information related to heart disease. Specifically, the neural network model may be learned based on a learning dataset including electrocardiograms and diagnostic results of heart disease, and based on correlations between various factors in the learning dataset.

The neural network model may be learned based on the electrocardiogram measured with 12 leads obtained from electrodes of an electrocardiogram measuring device connected to the human body. For example, an electrocardiogram can be measured with 12 leads of 10 seconds in length and stored at 500 points per second. Additionally, the neural network model can be learned based on partial information extracted from only 6 limb lead ECGs and a single lead (lead I) ECG among the 12 lead ECGs.

Specifically, the neural network model can receive ECG data as input, extract ECG features and classification information, and output ECG reading data based on the extracted ECG features and classification information. At least one convolutional neural network (CNN), arrangement It includes batch normalization and ReLU activation function layers, and may include a dropout layer. The neural network model may include a fully connected layer in which biological information such as age, gender, height, and weight is input as auxiliary information.

The neural network model may include a neural network corresponding to each of a plurality of leads of ECG data. That is, the neural network model may include an individual neural network into which electrocardiograms measured with individual leads are input.

Meanwhile, since the structure and type of neural network of the above-described neural network model are only examples, the neural network model according to an embodiment of the present invention may be configured in various ways based on the above-described examples.

The computing device 100 extracts an electrocardiogram graph representing the electrocardiogram waveform of the electrocardiogram data based on the extracted electrocardiogram features (S40), and visualizes it including heart animation data that is synchronized with the extracted electrocardiogram graph and visualizes the anatomical appearance of the heart. Content can be created (S50).

Referring to FIG. 9, when the electrocardiogram data is based on an electrocardiogram measured with 12 leads, the computing device 100 selects one representative electrocardiogram among the standard 12 lead electrocardiograms, and displays the selected representative electrocardiogram and an anatomical view of the heart. The heart animation data representing is synchronized and played. At this time, the computing device 100 includes the peak (start and end point) positions, duration, interval between waveforms, length, amplitude, and wave shape for the P wave, QRS complex, and T wave of the ECG waveform during one heart beat. Information is extracted, and the synchronization of heart movement, blood flow, or electrical flow is synchronized in the anatomical shape of the heart based on at least one of the extracted information.

For example, the computing device 100 uses location information about the start and end points of the P wave to express the anatomical shape of the heart so that the blood filling the left ventricle and the right atrium moves to the ventricle. Specifically, the computing device 100 calculates the time interval between the starting point of the P wave and the ending point of the P wave and sets it as the time when the blood filling the atrium passes to the ventricle, so that the anatomical shape of the heart moves.

In addition, the computing device 100 sets the ventricle to be filled with blood during the anatomical heart in the characteristic section from the point where the P wave ends (end point) to the point where the QRS complex begins, and the point where the QRS complex starts ( In the characteristic section at the point where the QRS complex ends (end point), the anatomical heart is set to fill the atrium with blood while emptying the blood that fills the ventricle, and at the point where the T wave begins (start point), T In the characteristic section at the point where the wave ends (end point), the atrium is set to be filled with blood in the anatomical view of the heart.

Through this process of synchronizing the electrocardiogram graph and heart animation data, the computing device 100 reproduces the interval and intensity of the electrical signal that causes the heart to beat in the electrocardiogram waveform of the electrocardiogram graph, and is synchronized with the electrocardiogram waveform to provide an anatomical appearance of the heart. It can show the process of blood circulation through the contraction and relaxation of the heart.

Additionally, the computing device 100 may play heart animation data by synchronizing the electrocardiogram and the electrical flow within the heart. As shown in Figure 10, the cardiac animation data is synchronized with the front part of the P wave to generate an electrical signal at the SA node, and is synchronized with the back part of the P wave, so the electrical signal generated at the SA node spreads through the left atrium and right atrium. In synchronization with the PR interval, the electrical signal is briefly delayed in the AV node, in synchronization with the QRS complex, the electrical signal spreads from the AV node along the His bundle, left bundle, and right bundle, and in synchronization with the T wave, the ventricular excitation recovery period occurs. The electrical signal is set to disappear.

Meanwhile, when it is determined that one of a plurality of heart diseases exists based on the classification information, the computing device 100 extracts at least one waveform information corresponding to the heart disease determined to exist from a pre-built database. . Additionally, the computing device 100 may generate an ECG graph by visualizing the waveform corresponding to classification information and waveform information in the ECG waveform of the ECG data so that at least one of color or shape is distinguishable from the remaining waveforms.

FIG. 11 is an exemplary diagram illustrating an ECG waveform correlated with a diagnosis result of heart disease according to an embodiment of the present disclosure, and FIG. 12 is a diagram illustrating a normal ECG waveform (a) and an abnormal ECG waveform (a) according to an embodiment of the present disclosure. This is an example diagram explaining the waveform (b), and FIG. 13 is an example diagram explaining an ECG graph in which ECG waveforms extracted based on the diagnosis results of heart disease are differentially displayed according to an embodiment of the present disclosure.

In general, ECG characteristics such as P wave, QRS complex, ST segment, and T wave width, height, cardiac axis (or electrical axis), and shape are used for ECG diagnosis and interpretation. The normal and abnormal standards for each ECG feature are used. The meaning is shown in Table 1 below. However, since Table 1 below is only an example, the present disclosure is not limited to Table 1.

electrocardiogram normal standard Meaning in case of abnormality P wave Electric shaft height
width 0~90°
<2.5mm
< 110ms Fresh ectopic beats outside the sinus node
Right atrium hypertrophy
Left atrial hypertrophy, interatrial conduction delay PR interval interval 120~200ms< 120ms: premature ventricular excitation >200ms: 1st degree atrioventricular conduction disorder QRS complex Electric shaft height
width

shape -30~90°
< 27mm (V _5,6 )
< 110ms

qRs, Rs, rS Fiber bundle blocking
left ventricular hypertrophy
Intraventricular conduction disorder, ventricular premature excitation
ventricular beat
Myocardial infarction (Q), right ventricular hypertrophy (qR, Rs in V1)
Ventricular premature excitation (ㅿ), ventricular beats ST segment height 0.5~2mm (80ms from point J) Elevation: Full-thickness (subepicardial) myocardial ischemic injury.
Descending: Subendocardial myocardial ischemic injury. T wave height electric axis More than 10% of R waves
Same direction as QRS complex Increased: hyperkalemia, subendocardial myocardial ischemia.
Symmetrical inversion: subepicardial myocardial ischemia.

For example, in the case of acute myocardial infarction, the waveform of ST segment elevation or development of Q wave appears, so the waveform information of the QRS complex and T wave is used as an ECG waveform that is highly correlated with acute myocardial infarction. Extracted from a pre-built database. If the QRS complex interval is longer than 0.12 seconds, it is abnormal and corresponds to right bundle branch block (RBBB) or left bundle branch block (LBBB). In the case of right bundle branch block, the dominant S wave in lead V1, Broad, monophasic R waves in the lateral leads (I, AVL, V5 and V6), prolonged R wave peak times exceeding 60 milliseconds (ms) in the left leads (V5, V6), and lateral leads (I, V5 , V6), a waveform in which the Q wave is absent appears, so the waveform information of the QRS complex is extracted from a pre-built database as an ECG waveform with a high correlation with right bundle branch block (see (b) in Figure 11).

In the case of left bundle branch block, an RSR' pattern (M-shaped QRS complex) appears in leads V1 to V3, and a wide, unclear S wave (W-shaped QRS complex) appears in the lateral leads (I, AVL, V5, V6). Therefore, the waveform information of the QRS complex is extracted from a pre-built database as an ECG waveform that has a high correlation with left bundle branch block (see Figure 11 (a)).

In the case of atrial tachycardia, the P wave, which has a different shape from the P wave shape seen in a normal ECG, is located in the front end, so the waveform information of the P wave is extracted from a pre-built database as an ECG waveform that has a high correlation with atrial tachycardia (Figure 11 (see (c) of).

In this way, the computing device 100 analyzes electrocardiogram waveforms such as P wave, QRS complex, and T wave as well as PR interval, QT interval, and ST segment, and stores waveform information corresponding to the diagnosis results of each heart disease in the database. You can use it.

As shown in FIG. 12, the computing device 100 can derive classification information for atrial fibrillation from ECG reading data estimated using a pre-trained neural network model, and provides an ECG that has a high correlation with the derived classification information. The waveform information of the P wave is extracted from a pre-built database. At this time, because it is difficult to visualize all of the standard 12 lead ECGs on one screen, the computing device 100 extracts one representative ECG among the standard 12 lead ECGs and generates the ECG through a neural network model based on the extracted representative ECG. Features can be extracted.

As shown in FIG. 13, the computing device 100 divides the P wave (A0), which is a waveform corresponding to classification information and waveform information in the ECG waveform during one heartbeat, into the remaining waveforms and one of the color, shape, size, or highlight effects. Create an electrocardiogram graph by visualizing at least one thing to be distinguished.

In addition, the computing device 100 is synchronized with the waveform extracted based on the classification information and waveform information, and determines the location of the heart corresponding to the classification information (e.g., atrial fibrillation) and the flow of blood or electrical signals to the anatomical heart. Heart animation data can be created by highlighting the shape in various forms such as color or highlight effects. At this time, the computing device 100 generates heart animation data visualizing the anatomical appearance of the heart using the heart axis calculated based on electrocardiogram characteristics.

FIG. 14 is a flowchart explaining a method of playing visualization content according to an embodiment of the present disclosure, and FIG. 15 is an example diagram showing an electrocardiogram graph according to an embodiment of the present disclosure. FIG. 16 is a diagram illustrating a process of synchronizing and playing back ECG graphs and heart animation data according to an embodiment of the present disclosure, and FIG. 17 is a diagram illustrating a normal standard of an ECG waveform based on ECG characteristics according to an embodiment of the present disclosure. This is a drawing to explain.

Referring to FIG. 14, the computing device 100 provides a user interface that can reproduce visualization content through a user terminal or an electrocardiogram monitor 10 including a display function. When the computing device 100 detects the start of playback of visualization content based on a user input such as a start button or touch (S110), it provides visualization content including an electrocardiogram graph and heart animation data.

As shown in FIG. 15, the computing device 100 displays and provides a reference point or baseline on the electrocardiogram graph, and the reference point or baseline is provided by a user such as touch and drag or click and drag. When the first event based on the input occurs (S120), heart animation data is provided so that the anatomical heart shape corresponding to the reference point or baseline of the electrocardiogram graph is displayed (S130).

As shown in FIG. 16, the computing device 100 synchronizes the electrocardiogram graph and heart animation data to reproduce the interval and intensity of the electrical signal generated during the heartbeat of the electrocardiogram waveform, along with changes in the electrocardiogram graph. The anatomical image of the heart is played in real time as heart condition information, including heart movement, blood flow, and electrical flow, changes from moment to moment (S140).

When playing visualization content on a device with a small display, the computing device 100 moves the ECG waveform by clicking and dragging the background screen of the ECG graph if all 10 seconds of ECG waveforms cannot be played on one screen, and moves the ECG waveform. Provides cardiac animation data of the anatomical heart shape according to the movement of the waveform.

At this time, the computing device 100 may reproduce visualization content such that at least one of the color, brightness, saturation, or highlight effect of the anatomical heart shape changes according to changes in the electrocardiogram graph.

If the computing device 100 identifies a waveform that is not included in the normal category based on the ECG characteristics and finds a waveform that is not included in the normal category (S150), the color of the waveform that is not included in the normal category; An electrocardiogram graph can be generated and displayed by modifying at least one of the shape or shape (S160). In addition, when a disease correlated with a waveform that is not included in the normal category is identified, the computing device 100 displays and provides visualization content showing the anatomical heart changed by the disease or abnormal heart movement or electrical flow. do.

As shown in FIG. 17, the computing device 100 uses a neural model based on rule-based machine learning to detect ECG waveforms in the ECG graph that are outside the normal standard in the ECG reading information obtained by analyzing the ECG data. The characteristics of the ECG waveform can be displayed in colors or patterns that are different from the normal ECG waveform.

In this way, the computing device 100 provides animated visualization content of the heart position or heart activity corresponding to abnormal results, so that users can increase their understanding of their own abnormal heart movement or disease and become more active in treatment. It can help you participate.

When a user input event occurs when a key input such as touch and drag or click and drag occurs (S170), the computing device 100 controls the movement of the electrocardiogram waveform or anatomical heart shape corresponding to the generated user input event. Or, execute a content control operation to change a specific configuration of the electrocardiogram waveform or anatomical heart shape (S180).

For example, the computing device 100 can display the path where electricity is generated, that is, the electric flow, in synchronization with the electrocardiogram graph, using a highlight effect, based on the voltage value of the electrocardiogram signal as well as the direction of the electric flow. You can also display the strength of electricity by brightness or size.

In addition, the computing device 100 displays a normal ECG waveform in which no disease exists when a second event occurs based on a user input that once specifies a waveform corresponding to classification information and waveform information in the ECG graph while the visualization content is stopped. Visualization content can be played as much as possible. The computing device 100 generates a third event based on a user input that specifies a waveform corresponding to the classification information and the waveform information in an electrocardiogram graph for a predetermined period (for example, 2 seconds or more) while the visualization content is stopped. When an occurrence occurs, the visualization content can be played so that the ECG waveform in which the disease corresponding to the classification information exists is displayed.

In addition, when an event occurs in which the user clicks once on a specific location such as the atrium, ventricle, left ventricle, or AV node of the anatomical heart while the heart animation data is stopped, the computing device 100 displays the corresponding image of the first heartbeat electrocardiogram. The ECG waveform of the part most similar to the part is played, and when two click events occur in succession, the ECG waveform corresponding to the second heartbeat ECG is played. That is, the computing device 100 can reproduce one of the ECG waveforms most closely associated with the heart region at a user's specific location. In general, looking at the correlation between the anatomical appearance of the heart and the electrocardiogram waveform, the left atrium and right atrium are associated with the P wave, the left and right ventricles are associated with the QRS complex, the wall area that distinguishes the left and right ventricles is associated with the Q wave, and the SA node is associated with the P wave. relationship appears high.

For example, when an event occurs based on a user input specifying the left atrium region, the computing device 100 may play visualization content so that the P wave most associated with left atrial activity is displayed.

In addition, when an event occurs based on a user input of selecting and zooming out an atrial region in the anatomical view of the heart, the computing device 100 causes blood to fill in the selected atrial region and displays an electrocardiogram waveform corresponding to the activity of the selected atrial region. You can play the visualization content so that it is displayed.

In this way, the present disclosure provides animated visualization content so that users without medical knowledge can easily understand it, thereby eliminating the need for a diagnosis by a professional medical staff to check the user's current heart health status, thereby reducing the cost of electrocardiogram diagnosis. savings can be achieved. In addition, the present disclosure can calculate the heart axis for each user using electrocardiogram data, and provide personalized heart animation data by reflecting the calculated heart axis, allowing the user to intuitively observe the appearance of his or her heart. Users can check changes in cardiac activity over time without the help of medical staff.

This disclosure allows hospitals and health check-up centers to use visualization content when explaining heart health to patients, and the anatomical heart changes in real time before, during, and after exercise by linking with exercise equipment with an electrocardiogram measurement function. It can also be visualized and shown.

The various embodiments of the present disclosure described above may be combined with additional embodiments and may be changed within the scope understandable to those skilled in the art in light of the above detailed description. The embodiments of the present disclosure should be understood in all respects as illustrative and not restrictive. For example, each component described as unitary may be implemented in a distributed manner, and similarly, components described as distributed may also be implemented in a combined form. Accordingly, all changes or modified forms derived from the meaning and scope of the claims of the present disclosure and their equivalent concepts should be construed as being included in the scope of the present disclosure.

Claims (17)

A method of providing visualization content based on electrocardiogram readings, performed by a computing device including at least one processor, comprising:
Obtaining electrocardiogram data;
Analyzing the ECG data to generate ECG reading data; and
Based on the generated electrocardiogram reading data, generating visualization content including an electrocardiogram graph representing an electrocardiogram waveform from the electrocardiogram data and heart animation data visualizing the anatomical appearance of the heart,
The heart animation data is,
Played in synchronization with the electrocardiogram graph and displaying heart condition information including at least one of heart movement, blood flow, or electrical flow,
method.
According to paragraph 1,
The electrocardiogram reading data is,
Containing classification information related to heart disease, calculated by a first model included in the pre-trained neural network model,
method.
According to paragraph 2,
The step of analyzing the ECG data to generate ECG reading data,
Based on the classification information, determining whether one or more of a plurality of heart diseases exists; and
When it is determined that one or more of the plurality of heart diseases exists through the classification information, extracting at least one waveform information corresponding to the heart disease determined to exist from a pre-built database;
Including,
method.
According to paragraph 3,
The step of analyzing the ECG data to generate ECG reading data,
extracting electrocardiogram features related to waveforms from the electrocardiogram data;
Including,
method.
According to paragraph 4,
The electrocardiogram characteristics are,
Including P waves, QRS complex, and T waves,
method.
According to paragraph 3,
The step of generating visualization content including an electrocardiogram graph representing the electrocardiogram waveform of the electrocardiogram data based on the generated electrocardiogram reading data and cardiac animation data visualizing the anatomical appearance of the heart,
generating the ECG graph by visualizing the waveform corresponding to the classification information and the waveform information in the ECG waveform of the ECG data so that at least one of color or shape is distinguishable from the remaining waveforms;
Including,
method.
According to clause 6,
The step of generating visualization content including an electrocardiogram graph representing an electrocardiogram waveform of the electrocardiogram data based on the generated electrocardiogram reading data and heart animation data visualizing the anatomical appearance of the heart,
In the ECG waveform of the ECG data, the classification information and the waveform corresponding to the waveform information are synchronized to visually highlight the anatomical heart shape corresponding to the heart disease determined to exist, thereby generating the heart animation data. steps;
Containing more,
method.
According to paragraph 4,
The step of generating visualization content including an electrocardiogram graph representing the electrocardiogram waveform of the electrocardiogram data based on the generated electrocardiogram reading data and cardiac animation data visualizing the anatomical appearance of the heart,
calculating a heart axis based on the ECG features extracted from the ECG data; and
generating the heart animation data by arranging the anatomical shape of the heart based on the calculated heart axis;
Including,
method.
According to clause 8,
The step of calculating the heart axis based on the ECG features extracted from the ECG data,
calculating a net amplitude of each standard lead and limb lead from the ECG data based on the ECG characteristics; and
calculating the angle of the heart axis based on a mathematical operation using the summed amplitude of the standard leads and the summed amplitude of the limb leads as input variables;
Including,
method.
According to clause 8,
The step of calculating the heart axis based on the ECG features extracted from the ECG data,
If the calculated heart axis is not included in the range of 45 degrees to 90 degrees based on induction I, generating the heart animation data by placing the anatomical heart shape based on 60 degrees;
Including,
method.
According to paragraph 4,
Further comprising providing a user interface for playing the visualization content to a user terminal,
method,
According to clause 11,
The step of providing a user interface for playing the visualization content to a user terminal,
When a first event occurs based on a user input for a preset reference point or baseline in the electrocardiogram graph, reproducing the visualization content so that an anatomical image of the heart corresponding to the reference point or baseline is displayed;
Including,
method.
According to clause 11,
The step of providing a user interface for playing the visualization content to a user terminal,
The visualization content such that at least one of the color, brightness, saturation, or highlight effect of the anatomical heart shape corresponding to the playback section changes in a section where the waveform corresponding to the classification information and the waveform information in the electrocardiogram graph is played. playing;
Including,
method.
According to clause 11,
The step of providing a user interface for playing the visualization content to a user terminal,
When a second event occurs based on a user input that once specifies a waveform corresponding to the classification information and the waveform information in the ECG graph while the visualization content is stopped, the visualization content displays a normal ECG waveform in which no disease exists. playing;
Including,
method.
According to clause 11,
The step of providing a user interface for playing the visualization content to a user terminal,
When a third event occurs based on a user input that specifies a waveform corresponding to the classification information and the waveform information in the ECG graph for a predetermined period while the visualization content is stopped, an ECG in which a disease corresponding to the classification information exists Playing the visualization content so that a waveform is displayed;
Including,
method.
A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, performs operations for providing visualization content based on electrocardiogram reading,
The above operations are:
An operation to obtain electrocardiogram data;
Analyzing the electrocardiogram data to generate electrocardiogram reading data; and
An operation of generating visualization content including an electrocardiogram graph representing an electrocardiogram waveform of the electrocardiogram data based on the generated electrocardiogram reading data and heart animation data visualizing the anatomical appearance of the heart;
The heart animation data is,
Played in synchronization with the electrocardiogram graph and displaying heart condition information including at least one of heart movement, blood flow, or electrical flow,
computer program.
A computing device for providing visualization content based on electrocardiogram readings,
A processor including at least one core; and
a memory containing program codes executable on the processor;
Including,
The processor, depending on execution of the program code,
Obtain electrocardiogram data,
Generates ECG reading data by analyzing the ECG data,
Based on the generated ECG reading data, generate visualization content including an ECG graph representing the ECG waveform of the ECG data and heart animation data visualizing the anatomical appearance of the heart,
The heart animation data is,
Played in synchronization with the electrocardiogram graph and displaying heart condition information including at least one of heart movement, blood flow, or electrical flow,
Device.

Images