Additional electrocardiogram measurement lead method

Abstract

The present disclosure may provide a method executed by a processor for inducing additional electrocardiogram measurements, the method including: acquiring a first type of electrocardiogram signal; and determining whether a second type of electrocardiogram signal, different from the first type of electrocardiogram signal, is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal.

Description

[Technical Field]

[0001] The present disclosure relates to methods for inducing additional measurements of electrocardiogram signals, and more particularly to methods for inducing additional measurements of other types of electrocardiogram signals. [Background technology]

[0002] An electrocardiogram is one of the basic diagnostic methods for heart disease and is an important examination method for diagnosing cardiovascular diseases such as angina pectoris, myocardial infarction, arrhythmia, etc. The heart is a three-dimensional organ, and in order to understand the pathological condition of such an organ using an electrocardiogram, it has been developed to obtain a large amount of information, usually by using multiple leads or by attaching it for a long period of time.

[0003] Electrocardiograms can be broadly divided into standard 12-lead electrocardiograms, which record signals using 10 electrodes, and Holter electrocardiograms, which record electrocardiograms continuously to observe intermittent abnormalities such as arrhythmia.Recently, wearable electrocardiograms (watch-type, patch-type, pad-type) have been developed that make measurement more convenient, but most only measure single-lead signals, and only some products have the function to measure limb lead signals.

[0004] Even if a wearable electrocardiograph has a limb lead function, it does not have a function related to the chest lead signals provided by a standard 12-lead electrocardiograph. Therefore, in the case of cardiac diseases where changes only appear clearly in the chest leads, it is difficult to diagnose the disease using only a single or limb lead.

[0005] Meanwhile, with the rapid advancement of deep learning technology, it is now possible to detect various diseases using only single or limb leads, even without chest lead signals. However, in the case of diseases where changes in chest leads are important for diagnosis, users face a problem of inconsistency between the signals obtained from the actual electrocardiograph and the disease diagnosed using deep learning technology, so ultimately users must confirm the information related to the chest leads. Therefore, it is believed that there is a need to develop an algorithm for guiding chest lead measurements. Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present disclosure is to provide a technique for inducing measurement of another type of electrocardiogram signal in addition to the basic type of electrocardiogram signal. For example, an object of the present disclosure is to provide a technique for identifying a situation in which measurement of a chest lead electrocardiogram signal is required in addition to a single lead electrocardiogram signal or a four limb lead electrocardiogram signal, and for providing feedback regarding such a situation. [Means for solving the problem]

[0007] To achieve the above-mentioned object, a method for inducing an additional electrocardiogram measurement according to an embodiment of the present disclosure is disclosed as a method executed by a processor for inducing an additional electrocardiogram measurement, which may include: acquiring a first type of electrocardiogram signal; and determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal.

[0008] As an alternative example, the first type of electrocardiogram signal may include a single-lead electrocardiogram signal or a limb lead electrocardiogram signal.

[0009] As an alternative example, the second type of electrocardiogram signal may include a precordial lead electrocardiogram signal.

[0010] As an alternative embodiment, the step of determining whether further electrocardiogram signals of the second type are required may include determining that further electrocardiogram signals of the second type are required if diagnostic information based on the first type of electrocardiogram signal corresponds to a predetermined type of disease.

[0011] In alternative embodiments, the predetermined type of disease may include at least one of diseases classified as being more sensitive in chest leads than in limb leads, or diseases classified as having different diagnostic methods for limb leads and chest leads.

[0012] As an alternative embodiment, the step of determining whether further electrocardiogram signals of the second type are required may include a step of determining that further electrocardiogram signals of the second type are required if the predicted value based on the first type of electrocardiogram signal is within a predetermined range from a reference value (or cut-off value).

[0013] As an alternative embodiment, the step of determining whether further electrocardiogram signals of the second type are required may include a step of predicting, based on the first type of electrocardiogram signal, a difference between first diagnostic information based on the first type of electrocardiogram signal and second diagnostic information based on the first type of electrocardiogram signal and the second type of electrocardiogram signal; and a step of determining whether further electrocardiogram signals of the second type are required based on the predicted difference.

[0014] As an alternative embodiment, the step of determining whether further electrocardiogram signals of the second type are required includes the steps of inputting the first type of electrocardiogram signal into a first prediction model having a first reference value to generate a predicted value; and determining whether further electrocardiogram signals of the second type are required based on the generated predicted value, wherein the second type of electrocardiogram signal may be for input into a second prediction model having a second reference value, and the first reference value may be set to be different from the second reference value.

[0015] As an alternative embodiment, the step of determining whether further electrocardiogram signals of the second type are needed may include a step of determining that further electrocardiogram signals of the second type are needed if diagnostic information based on the first electrocardiogram signal is generated two or more times within a predetermined period of time and the diagnostic information generated two or more times diagnoses electrocardiogram problems more than a predetermined number of times.

[0016] As an alternative embodiment, the step of determining whether or not further electrocardiogram signals of the second type are required may include the steps of: extracting waveforms of each of a plurality of lead signals included in the first type electrocardiogram signal; and determining whether or not further electrocardiogram signals of the second type are required based on differences in waveforms between the plurality of lead signals.

[0017] Also, there is provided a computer program stored on a computer-readable storage medium for solving the above-mentioned problem, which, when executed by one or more processors, performs an operation of inducing additional electrocardiogram measurements, and the operation may include an operation of acquiring a first type of electrocardiogram signal; and an operation of determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal.

[0018] Also, a computing device for inducing additional electrocardiogram measurements to solve the above-mentioned problem may include a processor including one or more cores; and a memory, wherein the processor acquires a first type of electrocardiogram signal; and determines whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal.

[0019] Also, a user equipment for solving the above problem may include at least one processor; and a memory, wherein the at least one processor is configured to determine whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required; and to generate feedback information related to additionally measuring the second type of electrocardiogram signal.

[0020] The technical solutions obtained from the present disclosure are not limited to the solutions described above, and a person having ordinary skill in the technical field to which the present disclosure pertains can clearly understand solutions other than the solutions described above from the following description. [Effects of the Invention]

[0021] The present disclosure can provide a technique for inducing measurement of an additional type of electrocardiogram signal (e.g., a chest lead electrocardiogram signal) in addition to a basic type of electrocardiogram signal (e.g., a single-lead electrocardiogram signal or a four-limb lead electrocardiogram signal). Therefore, if an additional type of electrocardiogram signal is needed to generate more accurate diagnostic information, feedback thereto can be generated, thereby enabling more accurate diagnostic results to be generated.

[0022] On the other hand, the effects obtained from the present disclosure are not limited to the effects described above, and a person having ordinary knowledge in the technical field to which the present disclosure pertains can clearly understand effects other than those described above from the following description. [Brief explanation of the drawings]

[0023] In order to facilitate understanding of the features of the present disclosure described above, several embodiments are described below as more detailed and specific examples, some of which are illustrated in the accompanying drawings. Furthermore, similar reference symbols in the drawings are intended to indicate the same or similar functions in multiple aspects. However, it should be noted that the accompanying drawings merely illustrate specific exemplary embodiments of the present disclosure and are not to be construed as limiting the scope of the present disclosure, and other embodiments having the same effect are also fully recognized.

[0024] [Figure 1] FIG. 1 is a block diagram of a computing device for directing additional electrocardiogram measurements, according to one embodiment of the present disclosure. [Figure 2] FIG. 1 is a conceptual diagram illustrating a neural network, according to one embodiment of the present disclosure. [Figure 3] 1 is a flowchart for guiding additional electrocardiogram measurements according to one embodiment of the present disclosure. [Figure 4] 1 is a flowchart for guiding additional electrocardiogram measurements based on disease type in one embodiment of the present disclosure. [Figure 5] 10 is a flowchart for guiding additional electrocardiogram measurements based on a preset reference value (or cut-off value) in one embodiment of the present disclosure. [Figure 6] 10 is a flowchart for guiding additional electrocardiogram measurements based on differences between diagnostic information in one embodiment of the present disclosure. [Figure 7] 10 is a flowchart for determining whether further electrocardiogram signals are needed based on quantitative diagnostic information in one embodiment of the present disclosure. [Figure 8] 10 is a flowchart for stimulating additional electrocardiogram measurements based on waveforms associated with multiple lead signals in one embodiment of the present disclosure. [Figure 9] FIG. 1 is a simplified general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0025] Various embodiments are described below with reference to the drawings. Various descriptions are provided herein to facilitate understanding of the present disclosure. However, it will be apparent that such embodiments can be practiced without such specific descriptions.

[0026] As used herein, terms such as "component," "module," and "system" refer to computer-related entities, hardware, firmware, software, a combination of software and hardware, or the execution of software. For example, a component can be, but is not limited to, a procedure running on a processor, a processor, an object, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a processor and / or thread of execution. A component can be localized within one computer. A component can be distributed across two or more computers. Such components can also execute on various computer-readable media having various data structures stored therein. Components can communicate via local and / or remote processes, for example, using signals comprising one or more data packets (e.g., data and / or signals from one component interacting with other components in a local or distributed system, or data transmitted over a network such as the Internet with other systems).

[0027] It should be noted that the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X utilizes A or B" is intended to mean one of the natural inclusive permutations. That is, if X utilizes A; X utilizes B; or X utilizes both A and B, then "X utilizes A or B" can apply to any of these. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more items among a list of associated items.

[0028] Additionally, the predicate "comprises" and / or the modifier "comprises" should be understood to mean the presence of the feature and / or component in question. However, the predicate "comprises" and / or the modifier "comprises" should be understood not to exclude the presence or addition of one or more other further features, components and / or groups thereof. Additionally, unless a specific number is specified or the context is clear that a singular form is indicated, the singular form in this specification and claims should generally be construed to mean "one or more."

[0029] Furthermore, the term "at least one of A or B" should be interpreted as meaning "when only A is included," "when only B is included," or "when a combination of A and B is included."

[0030] Those skilled in the art should further recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in accordance with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the various illustrative components, blocks, components, means, logic, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints of the overall system. Skilled artisans may implement the described functionality in various ways for each particular application. However, such implementation decisions should not be interpreted as departing from the scope of the present disclosure.

[0031] The description of the embodiments set forth herein is provided to enable one of ordinary skill in the art to utilize or practice the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present invention is not limited to the embodiments set forth herein. The present invention is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

[0032] In one embodiment of the present disclosure, the computing device may include a server and / or user equipment. In the present disclosure, the computing device may include any form of device having computing capabilities. The computing device is a digital device, and may be a digital device with a processor, memory, and computing capabilities, such as a laptop computer, notebook computer, desktop computer, web pad, mobile phone, etc. In one embodiment, if the computing device corresponds to a server, the computing device may be a web server that processes services. The above computing devices are merely examples, and the present disclosure is not limited thereto.

[0033] Data, as used in the detailed description of this disclosure and in the claims generally, can refer to material containing values ​​or information in any form, such as video, images, or text.

[0034] FIG. 1 is a block diagram of a computing device for directing additional electrocardiogram measurements, according to one embodiment of the present disclosure.

[0035] The configuration of the computing device (100) shown in Figure 1 is merely a simplified example. In one embodiment of the present disclosure, the computing device (100) may include other components for implementing the computing environment of the computing device (100), and the computing device (100) may be configured with only some of the disclosed components.

[0036] The computer device (100) may include a processor (110), a memory (130), and a network unit (150).

[0037] In one embodiment of the present disclosure, the processor 110 may be configured with one or more cores and may include processors for data analysis and deep learning, such as a computing central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU). The processor 110 may read a computer program stored in the memory 130 and execute data processing for machine learning in one embodiment of the present disclosure. According to one embodiment of the present disclosure, the processor 110 may perform calculations for neural network training. In deep learning (DL), the processor 110 may execute calculations for neural network training, such as processing input data for training, extracting features from the input data, calculating errors, and updating neural network weights using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor 110 may process network function training. For example, a CPU and a GPGPU may both train a network function or classify data using the network function. In one embodiment of the present disclosure, processors of multiple computing devices may be used together to train a network function or classify data using the network function. In one embodiment of the present disclosure, a computer program executed on a computing device may be a program executable by a CPU, a GPGPU, or a TPU.

[0038] In one embodiment of the present disclosure, the memory 130 can store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150.

[0039] In one embodiment of the present disclosure, the memory 130 may include at least one type of storage medium selected from the group consisting of flash memory, hard disk, micro multimedia card, card-type memory (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. The computing device 100 may also operate in conjunction with web storage that performs storage functions of the memory 130 over the Internet. The above descriptions of memory are merely examples, and the present disclosure is not limited thereto.

[0040] In the present disclosure, the network unit 150 can use any type of wired or wireless communication system.

[0041] The techniques described herein can be used in the above networks as well as other networks.

[0042] In one embodiment of the present disclosure, the processor 110 may perform operations to induce measurements of a second type of electrocardiogram signal in addition to a first type of electrocardiogram signal, where the first type of electrocardiogram signal may include a single-lead electrocardiogram signal or a limb lead electrocardiogram signal, and the second type of electrocardiogram signal may include a precordial electrocardiogram signal.

[0043] For example, the processor 110 may (1) perform an operation of acquiring a first type of electrocardiogram signal transmitted via the network unit 150, stored in the memory 130, or measured by a measurement unit unique to the computing device 100, and (2) perform an operation of determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal. (3) If the processor 110 determines that a second type of electrocardiogram signal is required, the processor 110 may generate feedback information to guide the user to perform an additional measurement of the second type of electrocardiogram signal. The feedback information may be generated in various forms, including visual information, auditory information, etc. The computing device 100 may further include a visual output unit, such as a display device, or an auditory output unit, such as a speaker, for outputting such feedback information. In addition to these output units, the computing device 100 may also include various output units for conveying feedback information to a user. Furthermore, the computing device (100) can transmit this feedback information to an external device such as a smart device via the network unit (150), and can also output this feedback information via the external device.

[0044] FIG. 2 is a schematic diagram illustrating a network function in one embodiment of the present disclosure.

[0045] Throughout this specification, the terms computational model, neural network, network function, and neural network may be used interchangeably. A neural network is often composed of a collection of interconnected computational units, commonly called nodes. Such nodes may also be referred to as neurons. A neural network is composed of at least one or more nodes. The nodes (or neurons) that make up a neural network may be interconnected by one or more links.

[0046] In a neural network, one or more nodes connected via links can form a relative relationship between an input node and an output node. The concepts of input node and output node are relative, and any node that is an output node for one node can also be an input node for another node, and vice versa. As mentioned above, the relationship between input node and output node can be established around links. One or more output nodes can be connected to one input node via links, and vice versa.

[0047] In a relationship between an input node and an output node connected via a link, the value of the data in the output node can be determined based on the data input to the input node. Here, the node interconnecting the input node and the output node can have a weight. The weight can be variable and can be changed by a user or an algorithm so that the neural network performs a desired function. For example, if one or more input nodes are interconnected to one output node by each link, the output node can determine its value based on the value input to the input node connected to the output node and the weight set for the link corresponding to each input node.

[0048] As described above, a neural network has one or more nodes interconnected via one or more links, forming a relationship between an input node and an output node within the neural network. The characteristics of a neural network can be determined by the number of nodes and links, the correlation between the nodes and links, and the weights assigned to each link. For example, if two neural networks have the same number of nodes and links but different link weights, the two neural networks can be recognized as different.

[0049] A neural network can be composed of a set of one or more nodes. A subset of the nodes in a neural network can form a layer. Some of the nodes in a neural network can form a layer based on their distance from a first input node. For example, a set of nodes whose distance from a first input node is n can form n layers. 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 the purpose of explanation, and the configuration of layers in a neural network can be defined in a manner different from the above description. For example, a layer of nodes can also be defined based on their distance from the final output node.

[0050] A first input node may refer to one or more nodes in a neural network to which data is directly input without passing through a link in relation to other nodes. Alternatively, a first input node may refer to a node in a neural network that does not have other input nodes connected via a link in relation to other nodes based on links. Similarly, a final output node may refer to one or more nodes in a neural network that do not have output nodes in relation to other nodes. Furthermore, a hidden node may refer to a node that is neither a first input node nor a final output node and that constitutes a neural network.

[0051] A neural network according to an embodiment of the present 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 and then increases again as the network progresses from the input layer to the hidden layer. A neural network according to an embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer is fewer than the number of nodes in the output layer, and the number of nodes decreases as the network progresses from the input layer to the hidden layer. Furthermore, a neural network according to another embodiment of the present 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 the network progresses from the input layer to the hidden layer. A neural network according to another embodiment of the present disclosure may be a neural network that combines the above-mentioned neural networks.

[0052] A deep neural network (DNN) can refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to understand the latent structures of data. That is, they can understand the latent structures of photos, text, video, audio, and music (e.g., whether a certain object appears in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). Deep neural networks 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, Siamese networks, and generative adversarial networks (GANs). The above-mentioned deep neural networks are merely examples and the present disclosure is not limited thereto.

[0053] In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network that outputs output data similar to input data. An autoencoder may include at least one hidden layer, and an odd number of hidden layers may be arranged 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 a bottleneck layer (encoder), and may expand in a symmetrical manner from the bottleneck layer toward the output layer (symmetrical to the input layer). An autoencoder may perform nonlinear dimensionality reduction. The number of input and output layers may correspond to the dimensionality of the input data after preprocessing. In an autoencoder structure, the number of nodes in the hidden layer included in the encoder may decrease as the distance from the input data increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, sufficient information may not be transmitted. Therefore, the number of nodes may be maintained above a certain number (e.g., more than half of the input layer).

[0054] Neural networks can be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Training a neural network can be a process of providing the neural network with knowledge that enables it to perform a specific operation. Neural networks can be trained in a direction that minimizes the error in the output. Training a neural network involves repeatedly inputting training data into the neural network, calculating the error between the neural network's output and the target for the training data, and backpropagating the neural network's error from the output layer to the input layer of the neural network to update the weights of each node in the neural network in a direction that reduces the error. In supervised learning, training data in which the correct answer is labeled is used for each individual piece of training data (i.e., labeled training data), while in unsupervised learning, the correct answer may not be labeled for each individual piece of training data.

[0055] For example, in supervised learning for data classification, training data can be data in which each training data is labeled with a category. The labeled training data is input to a neural network, and an error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in unsupervised learning for data classification, an error can be calculated by comparing the input training data with the output of the neural network.

[0056] FIG. 3 is a flow chart illustrating a method for guiding additional electrocardiogram measurements according to one embodiment of the present disclosure.

[0057] Referring to FIG. 3, a method for inducing additional electrocardiogram measurements may be performed by a computing device (100) and may include a step of acquiring a first type of electrocardiogram signal (S100), a step of determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal (S200), and a step of generating feedback information for inducing measurement of the second type of electrocardiogram signal if the second type of electrocardiogram signal is further required (S300).

[0058] An electrocardiogram (ECG) signal is a signal related to the action current and action potential difference accompanying the contraction of the heart, and can be a type of biosignal derived from electrical or magnetic biological signals. The ECG signal can be represented by a wavy line, and is usually displayed as multiple electrocardiogram curves using an electrocardiograph, and is very important in diagnosing the presence or absence of cardiac abnormalities.

[0059] The electrocardiogram signal has 10 electrodes and 12 lead signals that are combinations of the electrodes. The electrodes can include RA, LA, RL, LL, V1, V2, V3, V4, V5, and V6. The lead signals can include three standard limb leads, three augmented limb leads, and six precordial leads. The standard limb leads include leads I, II, and III, the augmented limb leads include aVR, aVL, and aVF, and the six precordial leads can include lead signals induced by chest electrodes V1, V2, V3, V4, V5, and V6. The lead signals are sometimes referred to as "leads" or "lead electrocardiogram signals."

[0060] Here, the first type of electrocardiogram signal may include a single-lead electrocardiogram signal or a limb-lead electrocardiogram signal. A single-lead electrocardiogram signal may be an electrocardiogram signal induced by only two electrodes, primarily measuring lead I, and may be easily measurable using a smartphone, smartwatch, portable patch, portable pad, other portable dedicated electrocardiogram measurement device, or a combination thereof. Therefore, in cases where an accurate diagnosis of arrhythmia is difficult with a single electrocardiogram test, measuring the single-lead electrocardiogram recorded during daily life can be very useful. Recording a patient's heartbeat during such activities can help diagnose arrhythmia and clarify the relationship between the patient's symptoms and arrhythmia, and can also provide information to determine the effectiveness of therapeutic drugs.

[0061] To measure standard limb leads, electrodes can be connected to both arms and one leg (mainly the left leg), including lead I (where the left and right arms are the anode and cathode), lead II (where one leg and the right arm are the anode and cathode, respectively), and lead III (where one leg and the left arm are the anode and cathode, respectively).

[0062] Meanwhile, the second type of electrocardiogram signal may include electrocardiogram signals from precordial leads, which, unlike the limb leads, allow observation of the direction and force of electrical flow in the heart from a left lateral view, and can complement the limb leads.

[0063] FIG. 4 is a flow chart for guiding additional electrocardiogram measurements based on disease type in one embodiment of the present disclosure.

[0064] 4, the step of determining whether the second type of electrocardiogram signal is further required (S200) may include a step of determining that the second type of electrocardiogram signal is further required (S290) if the diagnostic information based on the first type of electrocardiogram signal corresponds to a predetermined type of disease (S210). In particular, the predetermined type of disease may be a cardiac abnormality that is difficult to diagnose using only a single lead or limb leads.

[0065] Here, the predetermined type of disease may include diseases classified as being more sensitive in chest leads than in limb leads. For example, measuring chest leads for ischemic heart disease can improve diagnostic sensitivity and specificity compared to measuring only limb leads. Therefore, first, electrocardiograms are monitored using only the first type of electrocardiogram signal, which is relatively easy to use and includes a single lead or limb leads. If ischemic heart disease is suspected, the second type of electrocardiogram signal, which includes chest leads, can be further measured. This enables economical and continuous monitoring of ischemic heart disease and ensures diagnostic accuracy. Meanwhile, in this case, the step of determining whether or not the second type of electrocardiogram signal is further required (S200) may include a step of checking whether or not there is a correlation with ischemic heart disease, which is one of the predetermined types of diseases, in the diagnostic information based on the first type of electrocardiogram signal, and determining whether or not the second type of electrocardiogram signal is further required based on the checking result (S290). The step S290 may include the processor (110) analyzing the diagnostic information based on the first type of electrocardiogram signal, and determining that the second type of electrocardiogram signal is further required if ischemic heart disease is suspected.

[0066] The predetermined type of disease may include at least one disease classified as having different diagnostic methods for limb leads and chest leads. For example, left ventricular hypertrophy (LVH) is generally diagnosed by measuring chest leads, but it can also be diagnosed using limb leads. Therefore, electrocardiograms can be monitored using only the first type of electrocardiogram signal, which is relatively easy to use and includes a single lead or limb leads. If left ventricular hypertrophy is suspected, an additional second type of electrocardiogram signal including chest leads can be measured, enabling economical and continuous monitoring of left ventricular hypertrophy. Diagnostic accuracy can also be ensured by cross-validating the limb-lead-based diagnosis and the chest-lead-based diagnosis. The limb-lead-based diagnosis or the chest-lead-based diagnosis can be performed using a pre-trained artificial neural network diagnostic model or a rule-based method. Meanwhile, in this case, the step of determining whether or not the second type of electrocardiogram signal is further required (S200) may include a step of checking whether or not there is an association with left ventricular hypertrophy, which is one of the diseases classified as having different diagnostic methods for limb leads and chest leads, in the diagnostic information based on the first type of electrocardiogram signal, and determining whether or not the second type of electrocardiogram signal is further required based on the checking result (S290). The step S290 may include the processor (110) analyzing the diagnostic information based on the first type of electrocardiogram signal and determining that the second type of electrocardiogram signal is further required if left ventricular hypertrophy is suspected.

[0067] FIG. 5 is a flowchart for guiding additional electrocardiogram measurements based on a preset reference value (or cut-off value) in one embodiment of the present disclosure.

[0068] 5, the step of determining whether an additional second type of electrocardiogram signal is needed (S200) may include a step of determining that an additional second type of electrocardiogram signal is needed if the predicted value based on the first type of electrocardiogram signal is within a predetermined range from a reference value (or cut-off value) (S292). As an example, in a method of measuring the first type of electrocardiogram signal from a patient and comparing a predicted value calculated based on the electrocardiogram signal with a predetermined reference value (or cut-off value), waveform information related to the first type of electrocardiogram signal is acquired (S100), the predicted value calculated based on the waveform information related to the first type of electrocardiogram signal is set as a first predicted value, and a reference value (or cut-off value) for comparing with the first predicted value to provide information related to the risk of heart disease is set as the first reference value. In this case, in conventional methods, it is possible to provide information regarding the risk of heart disease based on a comparison between the first predicted value and the first reference value, but in the present disclosure, it is possible to compare the first predicted value with the first reference value (S220), and if the first predicted value is in the boundary region (or margin region) of the first reference value, it is determined that additional measurement of the second type of electrocardiogram is necessary (S292), and a signal to induce additional measurement of the second type of electrocardiogram is generated.

[0069] Specifically, in the boundary region (or margin region) of the first reference value, the lowest point of the region is set as the first-1 reference value, and the highest point of the region is set as the first-2 reference value. If the first reference value is less than the first-1 reference value, the processor 110 can determine (S291) that the risk of heart disease is normal and low. If the first reference value is equal to or greater than the first-1 reference value and less than the first-2 reference value, the processor 110 can determine (S292) that further second-type electrocardiogram signals are needed. If the first reference value is equal to or greater than the first-2 reference value, the processor 110 can determine (S293) that the risk of heart disease (e.g., cardiac arrest) is high. If the processor 110 determines that further second-type electrocardiogram signals are needed or that the risk of heart disease is high, the processor 110 can send a feedback signal to the user to further guide the user to take the second-type electrocardiogram.

[0070] The feedback signal can be sent to the user as a video / image, text information, or audio signal via a device connected directly to the computing device (100) or via a network.

[0071] In addition, in the present disclosure, a user can carry a device capable of measuring the first type of electrocardiogram signal, and if the device or an application embedded therein determines, based on the first type of electrocardiogram signal, that further measurement of the second type of electrocardiogram signal is necessary or that the user is at high risk for heart disease, the device or an application embedded therein can transmit data including the user's electrocardiogram information, personal identification information, or current location information to a server at a hospital or rescue organization.

[0072] FIG. 6 is a flowchart for guiding additional electrocardiogram measurements based on predicted differences between diagnostic information in one embodiment of the present disclosure.

[0073] In one embodiment of the present disclosure, the step (S200) of determining whether further electrocardiogram signals of the second type are needed may include a step (S230) of predicting a difference between "first diagnostic information" based on the first type electrocardiogram signal and "second diagnostic information" based on the first type electrocardiogram signal and the second type electrocardiogram signal; and a step (S290) of determining whether further electrocardiogram signals of the second type are needed based on the predicted difference.

[0074] Here, step S230 can be performed using a neural network model that receives the first type of electrocardiogram signal as input and predicts a difference between the first diagnostic information and the second diagnostic information. For example, step S230 can be performed using a neural network model (binary classification model) that, after receiving the first type of electrocardiogram signal, (i) outputs a true prediction value if the difference between the first diagnostic information and the second diagnostic information is predicted to be large (e.g., in a binary diagnosis such as positive / negative, the diagnoses are predicted to be different from each other, or in a quantitative diagnosis, the difference between diagnostic values ​​is predicted to exceed a predetermined threshold), and (ii) outputs a false prediction value if the difference between the first diagnostic information and the second diagnostic information is predicted to be small. In the above embodiment, step S290 can include a step of determining that the second type of electrocardiogram signal is further required if a true prediction value is generated in step S230. In other words, according to such an embodiment, when it is predicted that the difference between the first diagnostic information (e.g., diagnostic information based on a single or four-limb lead electrocardiogram signal) and the second diagnostic information (e.g., diagnostic information based on a 12-lead signal to which a chest lead electrocardiogram signal has been added) will be large, the user is prompted to additionally measure a second type of electrocardiogram signal (e.g., a chest lead electrocardiogram signal), thereby making it possible to prevent erroneous diagnostic information based on the first type of electrocardiogram signal (e.g., a single-lead electrocardiogram signal or a four-limb lead electrocardiogram signal) from being determined.

[0075] Meanwhile, the neural network model (binary classification model) that can be used in the above step S230 can be trained through an exemplary process including the following operations: (1) training a "first model that predicts diagnostic information based on a first type of electrocardiogram signal" and a "second model that predicts diagnostic information based on a first type of electrocardiogram signal and a second type of electrocardiogram signal," respectively; (2) inputting general electrocardiogram data into the first model and the second model, respectively, to make a prediction; (3) if the first model and the second model generate a predicted value with a large difference, extracting the data and labeling it as true, and if the first model and the second model generate a predicted value with a small difference, extracting the data and labeling it as false; and (4) training the neural network model (binary classification model) using the data labeled in this manner.

[0076] FIG. 7 is a flowchart for determining whether more electrocardiogram signals are needed based on quantitative electrocardiogram analysis information in one embodiment of the present disclosure.

[0077] Referring to FIG. 7, the step of determining whether further electrocardiogram signals of the second type are needed (S200) may include a step of determining that further electrocardiogram signals of the second type are needed (S290) if diagnostic information based on the first electrocardiogram signal is generated two or more times within a predetermined period (S110) and electrocardiogram problems are diagnosed more than a predetermined number of times based on the diagnostic information generated two or more times (S250).

[0078] For example, the processor (110) can use a neural network model that provides information for predicting or diagnosing the presence or absence of an electrocardiogram abnormality based on the first type of electrocardiogram signal (e.g., a single-lead electrocardiogram signal or a four-limb lead electrocardiogram signal) to perform N or more diagnoses (N is a natural number greater than or equal to 2) within a predetermined period (e.g., 1 hour, 12 hours, 1 day, etc.), and if K or more electrocardiogram problems (K is a natural number less than N) are diagnosed based on the diagnostic information generated N times, the processor (110) can guide the processor to perform additional measurements of the second type of electrocardiogram signal in order to guide a more precise diagnosis.

[0079] FIG. 8 is a flowchart for stimulating additional electrocardiogram measurements based on waveforms associated with multiple lead signals in one embodiment of the present disclosure.

[0080] Referring to FIG. 8, the step of determining whether or not the second type of electrocardiogram signal is further required (S200) includes the steps of extracting the waveform of each of the plurality of lead signals (310, 320) included in the first type of electrocardiogram signal, and determining whether or not the second type of electrocardiogram signal is further required (S290) based on the difference between the waveforms of the plurality of lead signals (310, 320).

[0081] Specifically, when P-QRS-T waveforms are extracted from Lead I (310) and Lead II (320), if there is a difference in the position, shape, or number of one or more of the extracted complexes, it is possible to suggest additional measurement using chest leads and determine whether a second type of ECG signal is required. In this case, the complex may include one or more of the PR interval, the QRS interval of the beat, the QT interval of the beat, or the R-peak position of the beat.

[0082] For example, the processor (110) can receive input of Lead I (310) and Lead II (320) signals from among the limb lead signals included in the first type electrocardiogram signal, set electrocardiogram coordinates for each signal, identify the p, q, r, s, and t coordinates of the beats therein, and generate waveform position information for multiple elements. Next, the processor (110) can perform a calculation to directly compare the waveform position information of Lead I with the waveform position information of Lead II, and if the value is equal to or greater than a set value, determine that an additional second type electrocardiogram signal is required.

[0083] Alternatively, the generated waveform position information can be used to derive values ​​of the PR interval, QRS interval, or QT interval, which are elements included in the signals of Lead I (310) and Lead II (320). If the difference in any one value of the PR interval, QRS interval, or QT interval of Lead I (310) and Lead II (320) is equal to or greater than a preset value, the processor 110 can determine that an additional second type of electrocardiogram signal is required.

[0084] As described above, when comparing the signals of Lead I (310) and Lead II (320) of the limb lead signals to automatically identify and notify whether chest leads are necessary, the limb lead signals can play a role of screening. As a result, the present disclosure can reduce the burden on medical staff, reduce the frequency with which chest lead signals need to be measured, and achieve the effect of enabling accurate monitoring of electrocardiogram signals.

[0085] Furthermore, in one embodiment of the present disclosure, the step of determining whether the second type of electrocardiogram signal is further required may include: (1) inputting the first type of electrocardiogram signal (e.g., a single-lead electrocardiogram signal or a four-limb lead electrocardiogram signal) into a first prediction model having a first reference value (or a first cut-off value) and predicting cardiovascular abnormalities to generate a predicted value; and (2) determining whether the second type of electrocardiogram signal (e.g., a chest lead electrocardiogram signal) is further required based on the generated predicted value.

[0086] In this case, the second type of electrocardiogram signal can be input to a second prediction model (e.g., a prediction model based on a 12-lead signal) that has a second reference value (or a second cut-off value) and predicts abnormalities in the cardiovascular system, and the second reference value can be set to be different from the first reference value.

[0087] The first and second reference values ​​may be set differently, taking sensitivity or specificity into consideration. For example, the first and second reference values ​​may be set differently so that the performance of the first prediction model has higher sensitivity than the second prediction model (e.g., if the sensitivity standard is 0.8-0.9, the first prediction model has a sensitivity of 0.9). In this case, the first prediction model may generate alarms for cardiovascular abnormalities more frequently than the second prediction model. This allows the first prediction model to be used as a means of early screening for diagnosing cardiovascular abnormalities. However, in this embodiment of the present disclosure, even if the first prediction model generates false positive alarms more frequently, a more accurate diagnosis can be achieved and fewer patients may be overlooked by prompting further measurement of the second type of electrocardiogram signal (e.g., chest lead electrocardiogram signal) for patients suspected of a particular disease.

[0088] FIG. 9 is a simplified general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure can be implemented.

[0089] While the present disclosure has been described above as generally being embodied in a computing device, those skilled in the art will appreciate that the present disclosure can also be embodied in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software.

[0090] Generally, modules herein include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Those skilled in the art will also appreciate that the methods of the present disclosure can be practiced with other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc., each of which can operate in conjunction with one or more associated devices.

[0091] The embodiments described in this disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0092] A computer includes a variety of computer-readable media. Any medium accessible by a computer can be computer-readable, including volatile and nonvolatile media, transitory and non-transitory media, and portable and non-portable media. By way of example and not limitation, computer-readable media can include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, portable and non-portable media implemented in any method or technology 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 technology, CD-ROM, digital video disk (DVD) or other optical disk storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store information.

[0093] Computer-readable transmission media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Any combination of any of the foregoing media should also be included within the scope of computer-readable transmission media.

[0094] An exemplary environment (1100) for implementing various aspects of the present disclosure is shown, including a computer (1102) including a processing unit (1104), a system memory (1106), and a system bus (1108). The system bus (1108) couples system components, including but not limited to the system memory (1106), to the processing unit (1104). The processing unit (1104) can be any of a variety of commercially available processors. Dual processors and other multi-processor architectures can also be utilized as the processing unit (1104).

[0095] The system bus (1108) can be any of several types of bus structures that can be further interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The 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. The BIOS contains the basic routines that support the exchange of information between the various components within the computer (1102), such as during startup. The RAM (1112) can also include high-speed RAM, such as static RAM, for caching data.

[0096] The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the 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 removable diskette 1118), and an optical disk drive 1120 (e.g., for reading from a CD-ROM disk 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 an external drive includes, for example, at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0097] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of computer 1102, the drives and media accommodate storing any data in a suitable digital format. While the foregoing description of computer-readable storage media refers to hard disk drives, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will appreciate that other types of computer-readable storage media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., can also be used in the exemplary operating environment, and that any such media can contain computer-executable instructions for performing the methods of the present disclosure.

[0098] A number of program modules, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136, may be stored on the drives and in RAM 1112. All or portions of the operating system, applications, modules, and / or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented with various commercially available operating systems or combinations of operating systems.

[0099] A user can enter commands and information into 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) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, and the like. 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 may also be connected through other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, and the like.

[0100] 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, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc.

[0101] The computer 1102 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and / or wireless communications. The remote computer(s) 1148 can be a workstation, a server computer, a router, a personal computer, a handheld computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and typically includes many or all of the components described for the computer 1102, although for simplicity, only a memory storage device 1150 is shown. The logical connections shown include wired and wireless connections in a local area network (LAN) 1152 and / or larger networks, e.g., a long-range network (WAN) 1154. Such LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global computer network, e.g., the Internet.

[0102] When used in a LAN networking environment, the computer 1102 connects to the local network 1152 through a wired and / or wireless communication network interface or adapter 1156. The adapter 1156 can facilitate wired or wireless communication to the LAN 1152, which may also include a wireless access point attached thereto for communicating with the wireless adapter 1156. When used in a WAN networking environment, the computer 1102 can include a modem 1158 or other means for establishing communications over the WAN 1154, such as connecting to a communications server on the WAN 1154 or through the Internet. The modem 1158, which can be internal or external and can be a wired or wireless device, connects to the system bus 1108 through the serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory / storage device 1150. It will be readily appreciated that the network connections shown are exemplary and other means of establishing a communications link between two or more computers may be used.

[0103] The computer 1102 is operable to communicate with any wireless device or unit configured and operating in a wireless manner, such as printers, scanners, desktop and / or handheld computers, portable data assistants (PDAs), communications satellites, any equipment or location associated with a radio-detectable tag, and telephones. This includes at least Wi-Fi and Bluetooth® wireless technologies. Thus, communication can be in a predefined structure, such as a traditional network, or simply ad hoc communication between at least two devices.

[0104] Wi-Fi (Wireless Fidelity) allows devices to connect to the Internet without being wired. Wi-Fi is a wireless technology similar to cell phones, allowing such devices, such as computers, to send and receive data indoors and outdoors—anywhere within the coverage area of ​​a base station. Wi-Fi networks use IEEE 802.11 (a, b, g, etc.) radio technology to provide secure, reliable, and fast wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 or 5 GHz radio bands, at data rates of, for example, 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual bands).

[0105] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips referred to in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields, etc. or particles, optical fields, etc. or particles, or any combination thereof.

[0106] Those skilled in the art will appreciate that the various illustrative logic blocks, modules, processors, means, circuits, and algorithm steps described in the description of the embodiments disclosed herein can be implemented with electronic hardware, various forms of program or design code (for convenience, referred to herein as "software"), or a combination of all of these. To clearly illustrate this interoperability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above generally by focusing on their functionality. Whether such functionality is implemented in hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art will appreciate that the described functionality can be implemented in various ways for each particular application, and such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0107] Various embodiments described herein may be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes any computer program, carrier, or media accessible by a 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.). Additionally, various storage media described herein include one or more devices and / or other machine-readable media for storing information.

[0108] It should be understood that the specific order or hierarchy of steps in the processes depicted is an example of an exemplary approach. Based on design priorities, it should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy depicted.

[0109] The description of the illustrated embodiments is provided to enable any person skilled in the art to which the disclosure pertains to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited by the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0110] As described above, the related content has been described based on the best mode for carrying out the invention.

Claims

1. 1. A processor-implemented method for inducing additional electrocardiogram measurements, comprising: acquiring an electrocardiogram signal of a first type; and determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal; Including, method.

2. In claim 1, the first type of electrocardiogram signal comprises a single lead electrocardiogram signal or a limb lead electrocardiogram signal; method.

3. In claim 1, the second type of electrocardiogram signal includes a precordial lead electrocardiogram signal; method.

4. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: determining that further electrocardiogram signals of the second type are required when diagnostic information based on the first type of electrocardiogram signals corresponds to a predetermined type of disease; method.

5. In claim 4, The predetermined type of disease includes at least one of a disease classified as having higher sensitivity in chest leads than in limb leads, or a disease classified as having different diagnostic methods for limb leads and chest leads, respectively. method.

6. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: determining that further electrocardiogram signals of the second type are required if the predicted value based on the first type of electrocardiogram signal is within a predetermined range from a reference value; method.

7. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: predicting, based on the first type of electrocardiogram signal, a difference between first diagnostic information based on the first type of electrocardiogram signal and second diagnostic information based on the first type of electrocardiogram signal and the second type of electrocardiogram signal; and determining whether further electrocardiogram signals of the second type are needed based on the predicted difference; Including, method.

8. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: inputting the first type of electrocardiogram signal into a first prediction model having a first reference value to generate a predicted value; and determining whether further electrocardiogram signals of the second type are needed based on the generated predicted values; Including, the second type of electrocardiogram signal is for input to a second prediction model having a second reference value; The first reference value is set to be different from the second reference value. method.

9. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: and determining that the second type of electrocardiogram signal is further required when the diagnostic information based on the first type of electrocardiogram signal is generated two or more times within a predetermined period and a predetermined number of electrocardiogram problems are diagnosed based on the diagnostic information generated two or more times. Including, method.

10. In claim 1, The step of determining whether further electrocardiogram signals of the second type are needed comprises: extracting a waveform of each of a plurality of lead signals included in the first type of electrocardiogram signal; and determining whether further electrocardiogram signals of the second type are required based on waveform differences between the plurality of lead signals; Including, method.

11. A computer program stored on a computer-readable storage medium, the computer program, when executed by one or more processors, performs operations for inducing additional electrocardiogram measurements, the operations comprising: acquiring an electrocardiogram signal of a first type; and determining whether a second type of electrocardiogram signal, different from the first type of electrocardiogram signal, is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal; Including, A computer program stored on a computer-readable storage medium.

12. 1. A computing device for inducing additional electrocardiogram measurements, comprising: a processor including one or more cores; and memory; Including, The processor: acquiring a first type of electrocardiogram signal; and determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required based on at least one of information related to the first type of electrocardiogram signal or diagnostic information based on the first type of electrocardiogram signal; Computing equipment.

13. User equipment, at least one processor; and memory; Including, The at least one processor determining whether a second type of electrocardiogram signal different from the first type of electrocardiogram signal is further required; and and additionally configured to generate feedback information related to making measurements of the second type of electrocardiogram signal. User equipment.

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