Methods for electrocardiogram description and integration of results

Abstract

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

Description

[Technical field]

[0001] The present disclosure relates to a method for processing medical data, and more particularly to a method for generating and integrating ECG delineation information relating to ECG data measured from one or more leads. [Background technology]

[0002] The electrocardiogram (ECG) waveform, which reflects the electrical activation phase of the heart, is basically composed of the P-wave, QRS-complex, and T-wave subregions. The P-wave of each heartbeat occurs during the phase of atrial depolarization, the QRS-complex reflects the phase of ventricular depolarization, and the T-wave reflects the phase of ventricular repolarization.

[0003] ECG description is a signal processing process that separates the above-mentioned detailed regions from ECG data. ECG description plays a very important role in ECG analysis because it can obtain detailed features of the heartbeat from the results of ECG description and information related to relative time intervals such as the amplitude of the detailed regions and the PR interval, ST interval, and QT interval, thereby enabling the presence or absence of abnormalities in the heartbeat rhythm and heart disease to be identified.

[0004] However, as the types of electrocardiogram measurement equipment have become more diverse (Standard 12-Lead, Holter, Mobile single-lead, Limb lead), and the types and number of leads used in electrocardiogram measurement, as well as the length of measurement time, vary, resulting in differences between the multiple data received from each device, making it difficult to stably derive electrocardiogram description information. U.S. Patent Publication No. 2021-0219920 discloses a system and method for internal electrocardiogram acquisition. Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure has been devised in response to the above-mentioned background art, and aims to provide a method for deriving stable electrocardiogram description information regardless of the number of leads of the measurement equipment and the length of measurement time. [Means for solving the problem]

[0006] According to an embodiment of the present disclosure to achieve the above object, a method for electrocardiogram description executed by one or more processors of a computing device is disclosed, which may include: receiving one or more electrocardiogram data; generating, from the received one or more electrocardiogram data, electrocardiogram delineation information corresponding to a plurality of samples each having a predetermined time length using an electrocardiogram description model; and integrating the plurality of electrocardiogram delineation information.

[0007] In an alternative embodiment, receiving the electrocardiogram data may include receiving the one or more electrocardiogram data from one of a plurality of different types of electrocardiogram measuring equipment.

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

[0009] In alternative embodiments, the predetermined length of time may be determined independently of the number of leads of the electrocardiogram measurement equipment or the length of the measurement time.

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

[0011] In an alternative embodiment, the step of aggregating the plurality of electrocardiogram descriptive information may include the steps of: generating smoothed electrocardiogram descriptive information for the plurality of samples; and aggregating the smoothed electrocardiogram descriptive information for the plurality of samples.

[0012] In an alternative embodiment, the step of generating smoothed electrocardiogram descriptive information for the plurality of samples may further comprise the step of deriving a representative value among a plurality of resulting values ​​of the electrocardiogram descriptive information for the plurality of samples within a fixed time interval.

[0013] In an alternative embodiment, the step of aggregating the smoothed electrocardiogram description information for the plurality of samples may further comprise the step of deriving a representative value from among a plurality of resulting values ​​of the smoothed electrocardiogram description information.

[0014] In an alternative embodiment, the present disclosure may further include extracting electrocardiogram characteristics from the integrated electrocardiogram descriptive information. The step of extracting electrocardiogram characteristics from the integrated electrocardiogram descriptive information may include dividing the integrated electrocardiogram descriptive information based on a time of one heartbeat, extracting each electrocardiogram characteristic from the electrocardiogram descriptive information divided by the time of one heartbeat, and deriving a representative value of each extracted electrocardiogram characteristic.

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

[0016] In an alternative embodiment, the electrocardiogram description method of the present disclosure further includes a step of generating a user interface (UI) including information related to the electrocardiogram description information, and the user interface may include: a first area displaying electrocardiogram data measured by the electrocardiogram measurement equipment; a second area displaying the electrocardiogram description information; and a third area displaying values ​​of electrocardiogram characteristics.

[0017] According to an alternative embodiment of the present disclosure, a computer program stored on a computer-readable storage medium including instructions for causing a computing device to perform operations may include: receiving one or more electrocardiogram data; generating electrocardiogram descriptive information from the received one or more electrocardiogram data using an electrocardiogram description model, the electrocardiogram descriptive information corresponding to a plurality of samples, each sample having a predetermined time length; and aggregating the plurality of electrocardiogram descriptive information.

[0018] According to an alternative embodiment of the present disclosure, a computing device is disclosed, which may include: a processor including one or more cores; a network unit for receiving one or more electrocardiogram data; and a memory; wherein the processor may include a process for receiving one or more electrocardiogram data, generating electrocardiogram description information from the one or more electrocardiogram data using an electrocardiogram description model, the electrocardiogram description information corresponding to a plurality of samples each having a predetermined time length, and integrating the electrocardiogram description information. Effect of the Invention

[0019] The present disclosure is capable of providing consistent electrocardiogram descriptive information regardless of the number of leads and the length of measurement time. [Brief description of the drawings]

[0020] The following drawings, which are attached for use in explaining the embodiments of the present disclosure, are merely a part of the embodiments of the present disclosure, and a person having ordinary knowledge in the technical field to which the present disclosure pertains (hereinafter referred to as an "ordinary engineer") can derive other drawings based on these drawings without making any effort to come up with a new invention. [Figure 1] FIG. 1 is a block diagram of a computing device that may perform operations to generate electrocardiogram descriptive information in accordance with one embodiment of the present disclosure. [Diagram 2] FIG. 2 is a schematic diagram illustrating a network function according to one embodiment of the present disclosure. [Diagram 3] FIG. 3 is a flow chart of a method for generating and integrating electrocardiogram description information according to one embodiment of the present disclosure. [Figure 4] FIG. 4 is a block diagram illustrating a process for generating electrocardiogram description information in a computing device according to one embodiment of the present disclosure. [Diagram 5] FIG. 5 is a conceptual diagram illustrating the process of smoothing electrocardiogram descriptive information according to one embodiment of the present disclosure. [Figure 6] FIG. 6 is a conceptual diagram illustrating electrocardiogram characteristics according to one embodiment of the present disclosure. [Figure 7] FIG. 7 is a block diagram of a computing device according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0022] Various embodiments are described below with reference to the drawings, in which like reference numerals are used to represent like components throughout the drawings. Various descriptions are provided herein to facilitate understanding of the present disclosure. However, the embodiments can undoubtedly be practiced without these specific descriptions. 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 software execution. 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, and a component can be localized within one computer or distributed across two or more computers. Such components can also execute from various computer-readable media having various data structures stored therein. Components can communicate, for example, through local and / or remote processing, by signals (e.g., data from one component interacting with other components in a local system, a distributed system, and / or data transmitted over a network such as the Internet to other systems) carrying one or more data packets.

[0023] 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 of the associated listed items.

[0024] Additionally, the predicate "comprises" and / or the modifier "comprises" should be understood to mean that the feature and / or component is present. However, the predicate "comprises" and / or the modifier "comprises" should be understood to not exclude the presence or addition of one or more other further features, components and / or groups thereof. Additionally, in the present specification and claims, the singular should generally be construed to mean "one or more," unless a specific number is specified or the context is clear to indicate the singular form.

[0025] Furthermore, the term "at least one of A or B" should be interpreted as meaning "a case in which only A is included," "a case in which only B is included," or "a case in which a combination of A and B is included."

[0026] Those skilled in the art should further appreciate that the various exemplary logical blocks, configurations, modules, circuits, means, logic, and algorithm steps described in accordance with the embodiments disclosed herein can be realized by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the various exemplary components, blocks, configurations, 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 limitations of the overall system. Skilled artisans can 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.

[0027] The description of the embodiments set forth herein is provided to enable one of ordinary skill in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited by the embodiments set forth herein, but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein. In this disclosure, the terms network function, artificial neural network, and neural network may be used interchangeably.

[0028] In contrast, in the detailed description and claims of this disclosure, the terms "image," "picture," or "video," or "image data," "image data," or "video data" refer to multidimensional data made up of discrete picture elements (e.g., pixels in a two-dimensional image), that is, to a visually perceptible object (e.g., as displayed on a video screen) or a digital representation of that object (e.g., a file corresponding to the pixel output of a CT, MRI detector, etc.).

[0029] For example, an "image," "picture," or "video" may be a medical image of a subject acquired by computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or any other medical imaging system known in the art. The image need not necessarily be provided for a medical purpose and may be provided for a non-medical purpose, such as, for example, an x-ray taken for security screening.

[0030] In the detailed description and claims of this disclosure, the "DICOM" (Digital Imaging and Communications in Medicine) standard is a general term for all standards used for digital image representation and communication in medical devices, and is published by a joint committee comprised of the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA).

[0031] In addition, in the detailed description and claims of this disclosure, "Picture Archiving and Communication System (PACS)" is a term referring to a system that stores, processes, and transmits medical images in accordance with the DICOM standard, and is capable of storing medical image data obtained using digital medical imaging devices such as X-rays, CT scans, and MRIs in DICOM format and transmitting it to terminals within the hospital or externally via a network, and enabling the addition of image interpretation results and medical records to the data.

[0032] FIG. 1 is a block diagram of a computing device for generating electrocardiogram result descriptive information according to one embodiment of the present disclosure.

[0033] 1 is merely a simplified example, and in one embodiment of the present disclosure, the computer device (100) may include other components for implementing the computing environment of the computer device (100), and the computer device (100) may be configured with only some of the disclosed components.

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

[0035] In an embodiment of the present disclosure, the processor (100) may be configured with one or more cores and may include a processor 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 an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor (110) may perform calculations for learning a neural network. In deep learning (DL), the processor (110) may execute calculations for learning a neural network, such as processing input data for learning, extracting features from the input data, calculating errors, and updating weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process learning of a network function. For example, a CPU and a GPGPU may both perform network function learning or data classification using the network function. In one embodiment of the present disclosure, processors of multiple computing devices may be used together to perform network function learning or data classification using the network function. In one embodiment of the present disclosure, a computer program executed in a computing device may be a program executable by a CPU, a GPGPU, or a TPU.

[0036] The processor 110 may receive one or more electrocardiogram data from a plurality of electrocardiogram measurement devices of different types and generate electrocardiogram description information. For example, the processor 110 may receive one or more electrocardiogram data from a plurality of electrocardiogram measurement devices having different numbers of leads or different measurement time lengths and generate electrocardiogram description information for each of the electrocardiogram measurement devices.

[0037] In an alternative embodiment, the processor may divide the electrocardiogram data based on a predetermined length of time determined independently of the number of leads of the electrocardiogram measurement equipment or the length of measurement time to generate a plurality of samples each having a predetermined length of time corresponding to the electrocardiogram data, where the predetermined length of time may be long enough to be received as input by the electrocardiogram descriptive model.

[0038] The processor (110) can input the divided samples, each having a preset time length, to the electrocardiogram description model and obtain, as an output, electrocardiogram description information generated for each sample. The input of the electrocardiogram description model can be electrocardiogram data of one fixed length, i.e., a preset time length. The output of the electrocardiogram description model can be electrocardiogram description information, i.e., information including position information of P waves, QRS complexes, and T waves of all beats included in the input samples. Here, "correspondence" can mean that one piece of electrocardiogram description information is obtained from each of the samples, and each sample and the electrocardiogram description information have a one-to-one correspondence. In this case, the number of electrocardiogram description information output can be the same as the number of input samples, each having a preset time length. The processor (110) can integrate the electrocardiogram descriptive information of the multiple samples, each having the above-mentioned preset time length, into one piece of electrocardiogram descriptive information. For example, after smoothing the electrocardiogram descriptive information of the multiple samples, the most frequent value at a specific time point can be used as the value of the integrated electrocardiogram descriptive information, but the present disclosure is not limited thereto. A specific description of the smoothing will be given later with reference to FIG. 5.

[0039] The processor 110 can extract electrocardiogram characteristics based on the integrated electrocardiogram description information. The electrocardiogram characteristics can include, but are not limited to, RR-interval, QRS duration, QT interval, or QT-corrected interval, which are used as a basis for determining heartbeat abnormalities and heart diseases. Since the integrated electrocardiogram description information includes multiple heartbeats, it is necessary to generate electrocardiogram description information divided into the time periods of each heartbeat in order to extract the electrocardiogram characteristics.

[0040] The processor (110) is capable of dividing the integrated electrocardiogram descriptive information based on the time of one heartbeat, extracting each electrocardiogram characteristic from each of the generated electrocardiogram descriptive information divided by the time of one heartbeat, and then deriving a representative value of each characteristic.

[0041] 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.

[0042] In one embodiment of the present disclosure, the memory (130) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), 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 a web storage that performs the storage function of the memory (130) over the Internet. The above description of the memory is merely exemplary, and the present disclosure is not limited thereto.

[0043] The network unit (150) according to an embodiment of the present disclosure can use various wired communication systems such as a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), Very High Speed ​​DSL (VDSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and a Local Area Network (LAN).

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

[0045] In one embodiment of the present disclosure, the network unit (150) may use any type of wired or wireless communication system.

[0046] The techniques described herein may be used in these networks as well as other networks.

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

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

[0049] Throughout this specification, the terms computation model, neural network, neural network model, sub-neural network model, network function, and neural network may be used interchangeably. A neural network may be composed of a collection of interconnected computational units, generally 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 compose a neural network may be interconnected by one or more links.

[0050] In a neural network, one or more nodes connected via links can form a relationship of input node and output node relative to each other. The concept of input node and output node is relative, and any node that is an output node for one node can be an input node in relation to another node, and vice versa. As mentioned above, the relationship between input node and output node can be established around the link. One or more output nodes can be connected to one input node via links, and vice versa. In the relationship between an input node and an output node connected via a link, the data of 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, when one or more input nodes are interconnected to one output node by each link, the output node can determine the value of the output node based on the value input to the input node connected to the output node and the weight set to the link corresponding to each input node.

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

[0052] A neural network may be composed of a set of one or more nodes. A subset of the nodes in a neural network may form a layer. A portion of the nodes in a neural network may 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 may form an n-layer. The distance from the first input node may be defined based on the minimum number of links that must be traversed to reach the node from the first input node. However, this definition of a layer is given arbitrarily for the purpose of explanation, and the configuration of layers in a neural network may be defined in a manner different from that described above. For example, a layer of nodes may be defined based on their distance from a final output node.

[0053] The 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, the first input node may refer to a node that does not have other input nodes connected via a link in relation between nodes based on links in a neural network. Similarly, the final output node may refer to one or more nodes in a neural network that do not have an output node in relation to other nodes. In addition, the 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.

[0054] The 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 input layer progresses to the hidden layer. The 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 smaller than the number of nodes in the output layer, and the number of nodes decreases as the input layer progresses to the hidden layer. Furthermore, the 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 input layer progresses to the hidden layer. The neural network according to another embodiment of the present disclosure may be a neural network in which the above-mentioned neural networks are combined.

[0055] Deep neural networks (DNNs) can refer to neural networks that include multiple hidden layers in addition to input and output layers. Deep neural networks can be used to understand the latent structures of data. In other words, they can understand the latent structures of photos, text, videos, audio, and music (e.g., whether an object is in the photo, what is the content and emotion of the text, what is the content and emotion of the audio, etc.). Deep neural networks can include convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, generative adversarial networks (GANs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), Q networks, U networks, Siamese networks, generative adversarial networks (GANs), etc. The above-mentioned deep neural networks are merely examples and the present disclosure is not limited thereto.

[0056] In one embodiment of the present disclosure, the network function may include an autoencoder. The autoencoder may be a type of artificial neural network for outputting output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be disposed 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 (encode), and may be expanded from the bottleneck layer toward the output layer (symmetrical to the input layer) in a manner symmetrical to the reduction. The autoencoder may perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the dimensions after preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layer included in the encoder may decrease as it gets farther from the input data. If the number of nodes in the bottleneck layer (the layer with the fewest number of nodes located between the encoder and the decoder) is too small, there is a possibility that a sufficient amount of information may not be transmitted, so it may be maintained at a certain number or more (e.g., more than half of the input layer, etc.).

[0057] A neural network can be trained by 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 to perform a specific operation.

[0058] Neural networks can be trained in a direction that minimizes the error of the output. In training a neural network, training data is repeatedly input to the neural network, the error of the neural network output and the target regarding the training data is calculated, and the weight value of each node of the neural network is updated by backpropagating the error of the neural network from the output layer of the neural network to the input layer as a direction to reduce the error. In the case of supervised learning, training data in which the correct answer is labeled is used for each training data (i.e., labeled training data), and in the case of unsupervised learning, the correct answer may not be labeled for each training data. That is, for example, the training data in supervised learning for data classification can be data in which each training data is labeled with a category. The labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the output of the neural network. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight value of each node in each layer of the neural network can be updated through backpropagation. The amount of change in the connection weight value of each node to be updated can be determined by a learning rate. The calculation of the neural network for the input data and the backpropagation of the error can constitute a learning cycle (epoch). The application method of the learning rate can be changed depending on the number of iterations of the neural network learning cycle. For example, in the early stage of neural network learning, the learning rate can be increased to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stage of learning, the learning rate can be decreased to increase accuracy.

[0059] In the training of a neural network, the training data may generally be a subset of the actual data (i.e., data to be processed using the trained neural network), and therefore there may be a training cycle in which the error associated with the training data decreases, but the error associated with the actual data increases. Overfitting is a phenomenon in which errors increase in the actual data due to excessive learning of the training data. For example, a neural network that has learned about cats by looking at a yellow cat may be unable to recognize that it is a cat when it sees a cat of a color other than yellow, which may be a type of overfitting. Overfitting may cause an increase in errors in machine learning algorithms. To prevent overfitting, various optimization methods may be applied. To prevent overfitting, methods such as increasing the training data, regularization, dropout, which deactivates some of the nodes of the network during the training process, and the use of a batch normalization layer may be applied.

[0060] FIG. 3 is a flow chart of a method for generating and integrating electrocardiogram description information according to one embodiment of the present disclosure.

[0061] At step S310, the processor (110) of the computing device (100) may receive at least one electrocardiogram data from an electrocardiogram measuring device. The electrocardiogram data may consist of one or more leads calculated based on a potential difference between two electrodes in the electrocardiogram measuring device. Here, the electrocardiogram data may refer to data received to the electrocardiogram measuring device via electrodes of the electrocardiogram measuring device or data received from electrodes directly connected to the computing device (100).

[0062] In step S320, the processor (110) may divide the electrocardiogram data received in step S310 into a number of samples having a certain length, and generate electrocardiogram description information for each of the divided samples, where the length of the samples may be a length that can be analyzed by an electrocardiogram description model to generate the electrocardiogram description information.

[0063] In step S330, the processor (110) may combine the electrocardiogram description information corresponding to the divided samples into one to generate a single electrocardiogram description information. In this case, the processor (110) may also perform a smoothing process for each of the divided samples to generate a single electrocardiogram description information. The specific smoothing process will be described later with reference to FIG. 5.

[0064] FIG. 4 is a block diagram illustrating a process for generating electrocardiogram description information in a computing device according to one embodiment of the present disclosure.

[0065] Electrocardiogram data (411) measured by one or more of the leads is received from one of a plurality of electrocardiogram measurement devices of different types. All of the electrocardiogram data (410) thus received can be divided into a length that can be analyzed by an electrocardiogram description model (420) to generate a plurality of samples. The electrocardiogram description model can be a deep learning model trained using electrocardiogram data including one or more labeling pairs among electrocardiogram description information as a training set, but the present disclosure is not limited thereto.

[0066] The divided electrocardiogram data samples 412 may be analyzed by an electrocardiogram description model 420 to generate electrocardiogram description information 431 corresponding to each of the samples. Each of the electrocardiogram description information 431 may include P-wave location information, QRS-complex location information, or T-wave location information.

[0067] On the other hand, the processor 110 can secondarily derive P-wave amplitude information, QRS-complex amplitude information, or T-wave amplitude information from the electrocardiogram description information 431 for each of the plurality of samples, but the present disclosure is not limited thereto.

[0068] Meanwhile, the processor 110 performs a smoothing process on the set of electrocardiogram description information 430 of the samples, followed by an integration process, which can generate a single electrocardiogram description information 440. The specific smoothing process will be described later with reference to FIG. 5.

[0069] Meanwhile, electrocardiogram characteristics can be extracted from the electrocardiogram description information (440). A specific description of electrocardiogram characteristics will be given later with reference to FIG. 6. The integrated electrocardiogram description information (440) can include multiple heartbeats, and the value of the electrocardiogram characteristics can vary slightly for each heartbeat. Therefore, in order to facilitate diagnosis of a patient, electrocardiogram characteristics can be extracted for each heartbeat from the integrated electrocardiogram description information, and a representative value of the electrocardiogram characteristics can be extracted from the extracted ones. For example, in order to extract the representative value, an average can be calculated for each electrocardiogram characteristic for each heartbeat, and characteristics that are farther away from the average than a multiple of the standard deviation of the electrocardiogram characteristics for each heartbeat can be removed, and the average can be calculated again and used as the representative value of each electrocardiogram characteristic.

[0070] On the other hand, in the above example, if there is an abnormality in heart rhythm or a heart disease, the method of extracting the electrocardiogram characteristics may change. For example, when an electrocardiogram is measured from a patient with atrial fibrillation, the value of the PR interval (PR-interval) among the electrocardiogram characteristics should not be defined because the maximum point (R-peak) of the R wave does not exist in the electrocardiogram characteristics of the patient's electrocardiogram. Therefore, in such a case, the ratio of the number of P waves (P-waves) to the number of maximum points of the R wave is calculated, and if the value does not exceed a threshold, it can be defined that the PR interval (PR-interval) does not exist among the electrocardiogram characteristics.

[0071] 5 is a conceptual diagram illustrating a process for smoothing electrocardiogram descriptive information according to an embodiment of the present disclosure. Referring to FIG. 5, the processor 110 may smooth electrocardiogram descriptive information of a plurality of samples to compensate for output noise of the measurement equipment for each lead and corresponding electrocardiogram descriptive information. For example, the computing device 100 may smooth the electrocardiogram descriptive information generated for each lead by specifying a window of a fixed sample length and applying the most frequent result value within the window to all windows, but the present disclosure is not limited thereto.

[0072] 6 is a conceptual diagram showing a number of electrocardiogram characteristics that can be extracted from typical electrocardiogram description data. The electrocardiogram characteristics are information related to segments that serve as indicators for grasping the presence or absence of abnormalities in heart rhythm and heart disease, and can include RR-interval, QRS duration (620), QT interval (650), or QT-corrected interval, but the present disclosure is not limited thereto.

[0073] In accordance with one embodiment of the present disclosure, a computer-readable storage medium having a data structure stored thereon is disclosed.

[0074] A data structure can refer to the organization, management, and storage of data that allows efficient access and modification of the data. A data structure can refer to the organization of data to solve a specific problem (e.g., data retrieval, data storage, data modification in the shortest time). A data structure can also be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements can include a connection relationship between data elements as thought by a user. A physical relationship between data elements can include an actual relationship between data elements that is physically stored in a computer-readable storage medium (e.g., a hard disk). A data structure can specifically include a collection of data, a relationship between data, and a function or command that can be applied to the data. An effectively designed data structure allows a computing device to perform calculations while minimizing the use of computing device resources. Specifically, a computing device can increase the efficiency of operations, reading, inserting, deleting, comparing, exchanging, and searching through an effectively designed data structure.

[0075] Data structures can be classified into linear data structures and non-linear data structures according to the type of the data structure. A linear data structure can be a structure in which only one piece of data is linked after another. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a series of data sets that are internally ordered. A list can include linked lists. A linked list can be a data structure in which data is linked in a manner in which each piece of data is linked in a row with a pointer. In a linked list, the pointer can contain information about the connection to the next or previous piece of data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list depending on the type of the data structure. A stack can be a data list structure that allows limited access to data. A stack can be a linear data structure in which data can be manipulated (e.g., inserted or deleted) only at one end of the data structure. Data stored in a stack can be a LIFO (Last in First Out) data structure in which the last in comes first. A queue is a data structure that allows limited access to data, and unlike a stack, it can be a FIFO (First in First out) data structure where the slowest data is stored is the slowest data is available. A deck can be a data structure that allows data to be processed on both ends of the data structure.

[0076] A nonlinear data structure may be a structure in which multiple pieces of data are concatenated after one piece of data. A nonlinear data structure may include a graph data structure. A graph data structure may be defined by vertices and edges, and the edges may include a line connecting two different vertices. A graph data structure may include a tree data structure. A tree data structure may be a data structure in which a path connecting two different vertices among multiple vertices included in a tree is one data structure. In other words, a graph data structure may be a data structure that does not form a loop.

[0077] Throughout this specification, the terms computation model, neural network, network function, and neural network can be used interchangeably. (Hereinafter, they will be uniformly described as neural network.) The data structure may include a neural network. The data structure including a neural network may be stored in a computer-readable storage medium. The data structure including a neural network may also include data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, and a loss function for training the neural network. The data structure including a neural network may include any of the components of the disclosed configurations. That is, the data structure including a neural network may include all or any combination of data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, and a loss function for training the neural network. In addition to the above configurations, the data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include all forms of data used or generated in the process of computing the neural network, and is not limited to the above. The computer-readable storage medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be comprised of a collection of interconnected computing units, generally referred to as nodes. Such nodes may be referred to as neurons. A neural network is comprised of at least one or more nodes.

[0078] The data structure may include data to be input to the neural network. The data structure including the data to be input to the neural network may be stored in a computer-readable storage medium. The data to be input to the neural network may include training data input during the training process of the neural network and / or input data to be input to the neural network after training has been completed. The data to be input to the neural network may include data that has undergone pre-processing and / or data to be pre-processed. Pre-processing may include a data processing process for inputting data to the neural network. Thus, the data structure may include data to be pre-processed and data generated in pre-processing. The above data structures are merely examples, and the present disclosure is not limited thereto.

[0079] The data structure may include weights of the neural network. (In this specification, weights and parameters may be used interchangeably.) The data structure including the weights of the neural network may be stored in a computer-readable storage medium. The neural network may include a plurality of weights. The weights are variable and may be varied by a user or an algorithm to allow the neural network to perform a desired function. For example, when one or more input nodes are interconnected to an output node by respective links, the output node may determine an output node value based on values ​​input to the input nodes connected to the output node and parameters set for the links corresponding to the respective input nodes. The above data structures are merely examples, and the present disclosure is not limited thereto.

[0080] By way of example and not limitation, the weights may include weights that are variable during the neural network training process and / or weights at which neural network training has been completed. The weights that are variable during the neural network training process may include weights at the start of a learning cycle and / or weights that are variable during a learning cycle. The weights at which neural network training has been completed may include weights at which a learning cycle has been completed. Thus, a data structure including weights of a neural network may include a data structure including weights that are variable during the neural network training process and / or weights at which neural network training has been completed. Thus, the above-mentioned weights and / or combinations of each weight are included in a data structure including weights of a neural network. The above-mentioned data structures are merely exemplary and the present disclosure is not limited thereto.

[0081] The data structure including the neural network weights may be stored in a computer-readable storage medium (e.g., memory, hard disk) after a serialization process. Serialization may be a process of converting a data structure into a form that can be stored in the same or another computing device and later reconstructed for use. The computing device may serialize the data structure and transmit and receive the data over a network. The serialized data structure including the neural network weights may be reconstructed in the same or another computing device through deserialization. The data structure including the neural network weights is not limited to serialization. Furthermore, the data structure including the neural network weights may include a data structure for increasing the efficiency of calculations while minimizing the use of computing device resources (e.g., nonlinear data structures such as B-Tree, Trie, m-way search tree, AVLtree, Red-Black Tree). The above is merely an example, and the present disclosure is not limited thereto.

[0082] The data structure may include hyper-parameters of the neural network. The data structure including the hyper-parameters of the neural network may be stored in a computer-readable storage medium. The hyper-parameters may be variables that are changed by a user. The hyper-parameters may include, for example, a learning rate, a cost function, a number of learning cycle iterations, weight initialization (e.g., setting a range of weights to be initialized), and the number of Hidden Units (e.g., the number of hidden layers, the number of nodes in the hidden layers). The above data structures are merely examples, and the present disclosure is not limited thereto.

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

[0084] Although 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 capable of being executed on one or more computers and / or as a combination of hardware and software.

[0085] Generally, modules herein include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Additionally, those skilled in the art will 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.

[0086] 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.

[0087] A computer includes a variety of computer-readable media. Any medium accessible by a computer can be a computer-readable medium, including volatile and non-volatile media, transitory and non-transitory media, mobile and non-mobile 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, mobile and non-mobile 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, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be accessed by a computer and used to store information.

[0088] 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 above media should also be included within the scope of computer-readable transmission media.

[0089] 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).

[0090] 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, and contains the basic routines that support the exchange of information between the various components of the computer (1102), such as during start-up. The RAM (1112) can also include high-speed RAM, such as static RAM, for caching data.

[0091] The computer 1102 also includes an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA) - although 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 CD-ROM disks 1122 and 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 the Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0092] These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, the drives and media accommodate the storage of any data in a suitable digital format. While the above description of computer-readable storage media refers to HDDs, 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, and the like, can also be used in the exemplary operating environment, and further that any such media can contain computer-executable instructions for performing the methods of the present disclosure.

[0093] A number of program modules may be stored in the drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. 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.

[0094] A user may 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 that is 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.

[0095] A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface, such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown), such as speakers, printers, etc.

[0096] 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 depicted for the computer 1102, although for simplicity, only a memory storage device 1150 is illustrated. The logical connections depicted include wired and wireless connections in a local area network (LAN) 1152 and / or larger networks, such as a wide area 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, such as the Internet.

[0097] 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 installed thereon for communicating with the wireless adapter 1156. When used in a WAN networking environment, the computer 1102 can include a modem 1158 or have 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 a 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 the computers may be used.

[0098] The computer 1102 is operable to communicate with any wireless device or unit that is deployed and operates in wireless communication, such as printers, scanners, desktop and / or handheld computers, portable data assistants (PDAs), communication satellites, any equipment or location associated with a wirelessly 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.

[0099] Wi-Fi (Wireless Fidelity) allows devices to connect to the Internet without being wired. Wi-Fi is a wireless technology like a cell phone that allows such devices, e.g., computers, to send and receive data indoors and outdoors, anywhere within the range 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, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands, e.g., at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual bands).

[0100] Those of ordinary skill 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.

[0101] Those of ordinary skill in the art will appreciate that the various exemplary logic blocks, modules, processors, means, circuits, and algorithm steps described in the description of the embodiments disclosed herein can be implemented by 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 such interoperability of hardware and software, the various exemplary components, blocks, modules, circuits, and steps have been generally described above with a focus on their functionality. Whether such functionality is implemented in hardware or software depends on the design constraints imposed on the particular application and the overall system. Those of ordinary skill 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 departing from the scope of the present disclosure.

[0102] The various embodiments disclosed herein may be implemented by a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. The term "article of manufacture" includes a computer program, carrier, or media accessible by any computer-readable device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, the various storage media disclosed herein may include one or more devices and / or other machine-readable media for storing information.

[0103] It should be understood that the particular order or hierarchy of the stages in the processes depicted is an example of an exemplary approach. Based on design priorities, it should be understood that the particular order or hierarchy of the stages in the processes can be rearranged within the scope of the present disclosure. The accompanying method claims provide elements of the various stages in a sample order, but are not meant to be limited to the particular order or hierarchy depicted.

[0104] The description of the illustrated embodiments is provided to enable any person of ordinary skill in the art to which the disclosure pertains to the present disclosure to utilize or practice the present disclosure. Various modifications to such embodiments will be apparent to those of ordinary skill in the art to which the disclosure pertains, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited by the embodiments shown herein, but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. 1. A method executed by one or more processors of a computing device, comprising: receiving one or more electrocardiogram data; generating electrocardiogram delineation information from the one or more received electrocardiogram data using an electrocardiogram delineation model, the electrocardiogram delineation information corresponding to a plurality of samples each having a predetermined time length; and integrating said plurality of electrocardiogram descriptive information; Including, method.

2. In claim 1, receiving the electrocardiogram data includes receiving the one or more electrocardiogram data from one of a plurality of different types of electrocardiogram measurement equipment. method.

3. In claim 1, The predetermined time period is determined independently of the number of leads of the electrocardiogram measuring device or the length of the measuring time. method.

4. In claim 1, The step of generating electrocardiogram descriptive information corresponding to a plurality of samples, each having a predetermined time length, from the one or more received electrocardiogram data using the electrocardiogram descriptive model includes: confirming the number of leads of the electrocardiogram measuring device and the length of the measuring time; generating the plurality of samples by dividing the one or more electrocardiogram data based on the predetermined length of time; and generating respective electrocardiogram description information corresponding to the plurality of samples using the electrocardiogram description model; Including, method.

5. In claim 1, The electrocardiogram description information comprises: P-wave position information QRS-complex location information; or T-wave position information; At least one of the following is included: The processor determines from the electrocardiogram description information P wave amplitude information; QRS complex amplitude information; or T wave amplitude information Derive at least one of method.

6. In claim 1, The step of aggregating the plurality of electrocardiogram descriptive information comprises: generating smoothed electrocardiogram descriptive information relating to the plurality of samples; and aggregating the smoothed electrocardiogram descriptive information for the plurality of samples; Including, method.

7. In claim 6, The step of generating smoothed electrocardiogram descriptive information relating to the plurality of samples comprises: deriving a representative value from among a plurality of resulting values ​​of electrocardiographic description information relating to said plurality of samples within a fixed time interval; Further comprising: method.

8. In claim 6, The step of aggregating the smoothed electrocardiogram description information for the plurality of samples comprises: deriving a representative value from among a plurality of result values ​​of the smoothed electrocardiogram description information; Further comprising: method.

9. In claim 1, The method comprises: extracting electrocardiogram characteristics from the combined electrocardiogram description information. Further including, The step of extracting electrocardiogram features from the integrated electrocardiogram description includes: dividing the integrated electrocardiogram description information based on the time of one heartbeat; extracting electrocardiogram characteristics from the electrocardiogram description information divided by time of one cardiac beat; and deriving a representative value for each extracted electrocardiogram feature; Including, method.

10. In claim 9, The electrocardiogram characteristic may be RR-interval; QRS duration; QT interval; or QT-corrected interval; At least one of the following is included: method.

11. In claim 1, The method comprises: generating a user interface (UI) including information related to the electrocardiogram description information; Further comprising: The user interface includes: a first area for displaying electrocardiogram data measured by the electrocardiogram measuring device; a second area for displaying said electrocardiogram descriptive information; and a third area displaying the value of the electrocardiogram characteristic; Including, method.

12. A computer program stored on a computer readable storage medium comprising instructions that cause a computing device to perform operations, the operations being: receiving one or more electrocardiogram data; generating electrocardiogram description information from the one or more received electrocardiogram data using an electrocardiogram description model, the electrocardiogram description information corresponding to a plurality of samples, each sample having a predetermined time length; and integrating said plurality of electrocardiogram descriptions; Including, A computer program stored on a computer readable storage medium.

13. 1. A computing device comprising: A processor including one or more cores; A network portion for receiving one or more electrocardiogram data; and memory; Including, The processor, receiving one or more electrocardiogram data; generating electrocardiogram descriptive information from the one or more received electrocardiogram data corresponding to a plurality of samples, each sample having a predetermined time length, using an electrocardiogram descriptive model; and aggregating the plurality of electrocardiogram descriptive information; Computing device.

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