KR20240068024A - Server for managing electrocardiogram database of electrocardiogram device, method and program - Google Patents

Abstract

The present disclosure provides a method for managing an ECG database of an ECG device performed by a server, comprising: receiving an XML file of ECG data from the ECG device; Classifying XML files of ECG data by type; Selecting first tag information containing information that needs to be parsed within the XML file among XML files classified by type; Selecting second tag information corresponding to the waveform type of ECG data from among the first tag information; Creating a data table based on at least one of patient information and patient-specific test information in the XML file; creating a data table; Saving a data table and converting it into a database; and extracting the waveform data from the database into an analyzable form.

Description

Server, method, and program for managing an electrocardiogram database of an electrocardiogram device {SERVER FOR MANAGING ELECTROCARDIOGRAM DATABASE OF ELECTROCARDIOGRAM DEVICE, METHOD AND PROGRAM}

This disclosure relates to a server for managing an electrocardiogram database of an electrocardiogram device. More specifically, the present disclosure relates to a server, method, and program for managing an electrocardiogram database of an electrocardiogram device.

Electrocardiogram is one of the most basic non-invasive tests used in patient treatment.

The vital sign measurement equipment used in hospitals consists of specially certified products.

The electrocardiogram produces clinically meaningful results and is important data that affects patient treatment.

In general, ECG data exists independently rather than being linked to electronic medical records. ECG data generated through an ECG device may include the patient's personal information.

However, ECG data is a text document based on an XML file, and it is difficult to link it directly to a database.

Accordingly, in the existing method, the data manager directly selected the files for patients requested by the researcher and exported the files.

These data are text documents that follow formats such as XML, and since text documents are semi-structured data, there is a limitation in that it takes a relatively long time to extract necessary data and pseudonymize patient data before being converted into a database.

However, when the contents of the XML file are converted into a database with each tag as a column name and the sub-contents as the values, the use of RDBMS query statements greatly reduces the information that can be missed or additionally imported when searching for patients, and The speed of exporting files to researchers is relatively fast, and a higher level of security is possible than storing them as files.

Additionally, waveform data stored in the database can be used for actual research, etc. through a separate decoding module.

US Patent Publication US 8874453 (announced on October 28, 2014)

Embodiments disclosed in the present disclosure can construct a large-capacity database by parsing an XML file generated from an ECG device, thereby providing easy processing of large-capacity data, and decoding and extracting waveform data stored in the database. The purpose is to provide effects that are easy to use in actual research, etc.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

An ECG database management method of an ECG device performed by a server according to an aspect of the present disclosure for achieving the above-described technical problem is a method of managing an ECG database of an ECG device performed by a server, wherein ECG data is collected from the ECG device. Receiving the XML file; Classifying the XML file of the ECG data by type; Selecting first tag information containing information that needs to be parsed within the XML file among the XML files classified by type; Selecting second tag information corresponding to a waveform type of ECG data from among the first tag information; Creating a data table based on at least one of patient information and patient-specific test information in the XML file; Saving the data table and converting it into a database; and extracting the waveform data from the database into an analyzable form.

Additionally, the second tag information selection step may be characterized by selecting tag information related to 'RestingECG.Waveform' in the first tag information.

Additionally, the table creation step may be characterized by converting the Base64 encoded data into a database as is.

In addition, the step of extracting waveform data into an analyzable form includes decoding each waveform data recorded for each ECG electrode of the ECG device by time slot when converting the Base64 encoded data into analyzable waveform data. It can be characterized as:

Additionally, the electrocardiogram data may be standard 12-lead electrocardiogram data.

In addition, a server for managing an ECG database of an ECG device according to another aspect of the present disclosure includes: a memory storing a data table related to management of the ECG database; and a processor that controls operations related to management of the ECG database, wherein the processor receives an XML file of ECG data from the ECG device, classifies the XML file of the ECG data by type, and classifies the XML file by type. Among the XML files, first tag information containing information that needs to be parsed is selected, second tag information corresponding to the waveform type of the ECG data is selected among the first tag information, and the selected Generating a data table based on information, creating a data table based on at least one of patient information and examination information for each patient in the XML file, storing the data table into a database, and analyzing waveform data in the database. It can be characterized as being extracted in a form.

Additionally, the processor may select tag information related to 'RestingECG.Waveform' in the first tag information when selecting the second tag information.

Additionally, when creating the data table, the processor may convert the Base64-encoded data into a database as is.

In addition, when extracting waveform data into an analyzable form and converting the Base64 encoded data into analyzable waveform data, the processor separates each waveform data recorded for each ECG electrode of the ECG device into a time zone. It can be characterized by decoding by star.

Additionally, the electrocardiogram data may be standard 12-lead electrocardiogram data.

In addition, a computer program stored in a computer-readable storage medium according to another aspect of the present disclosure, when executed on one or more processors, performs the following operations for performing an ECG database management method of an ECG device, the operations These include: receiving an XML file of ECG data from the ECG device; Classifying the XML file of the ECG data by type; An operation of selecting first tag information containing information that needs to be parsed within the XML file among the XML files classified by type; An operation of selecting second tag information corresponding to a waveform type of ECG data from among the first tag information; Creating a data table based on at least one of patient information and patient-specific test information in the XML file; An operation of storing the data table and converting it into a database; and extracting waveform data from a database in an analyzable form.

Additionally, the second tag information selection operation may be characterized by selecting tag information related to 'RestingECG.Waveform' in the first tag information.

Additionally, the table creation operation may be characterized by converting the Base64 encoded data into a database as is.

In addition, the operation of extracting the waveform data into an analyzable form includes decoding each waveform data recorded for each ECG electrode of the ECG device by time zone when converting the Base64 encoded data into analyzable waveform data. It can be characterized as:

Additionally, the electrocardiogram data may be standard 12-lead electrocardiogram data.

In addition, a computing device for managing an ECG database of an ECG device according to another aspect of the present disclosure includes a processor including one or more cores; and a memory storing instructions executable by the processor, wherein when the instructions are executed by the processor, the processor receives an XML file of ECG data from the ECG device, and stores the ECG data. Classify XML files by type, select first tag information containing information that needs to be parsed within the XML file among the XML files classified by type, and select the waveform type of ECG data among the first tag information. Select the corresponding second tag information, create a data table based on the selected information, create a data table based on at least one of patient information and test information for each patient in the XML file, and create the data table. It may be stored and converted into a database, and may be characterized by extracting waveform data from the database in an analyzable form.

Additionally, the processor may select tag information related to 'RestingECG.Waveform' in the first tag information when selecting the second tag information.

Additionally, when creating the data table, the processor may convert the Base64-encoded data into a database as is.

In addition, when extracting waveform data into an analyzable form and converting the Base64 encoded data into analyzable waveform data, the processor separates each waveform data recorded for each ECG electrode of the ECG device into a time zone. It can be characterized by decoding by star.

Additionally, the electrocardiogram data may be standard 12-lead electrocardiogram data.

In addition to this, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the means for solving the above-described problem of the present disclosure, a large-capacity database can be built by parsing an XML file generated from an ECG device, thereby providing the effect of facilitating large-capacity data processing. In addition, since waveform data stored in the database can be decoded and extracted, it provides an effect that is easy to use in actual research, etc.

1 is a diagram showing an electrocardiogram database management system of the present disclosure.
Figure 2 shows the configuration of the server of Figure 1.
Figure 3 is a flowchart showing the ECG database management method of the present disclosure.
FIG. 4 is a diagram showing an example of a process of storing first tag information in a data table in the processor of FIG. 2.
FIG. 5 is a diagram showing an example of a process of storing second tag information in a data table in the processor of FIG. 2.
FIG. 6 is a diagram showing the results databased by the processor of FIG. 2.
FIG. 7 is a diagram showing the result of decoding waveform data by the processor of FIG. 2.

Like reference numerals refer to like elements throughout this disclosure. This disclosure does not describe all elements of the embodiments, and general content or overlapping content between embodiments in the technical field to which this disclosure pertains is omitted. The term 'part, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'part, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In the existing file export method for ECG data, the data manager directly selected files for patients requested by researchers and exported the files.

However, in the conventional file export method for ECG data, the data manager directly selects the files, so even if there are files that are missing or incorrectly imported during the file selection process, it is difficult to identify them. Because the ECG data produced through this process includes the patient's personal information, accurate patient inquiry and security were required.

Therefore, the conventional file export method for ECG data is that the ECG data is a text document that follows a format such as XML, and the text document corresponds to semi-structured data, so it takes relatively long time to extract the necessary data and pseudonymize the patient data before it is converted into a database. Because it took a long time, the speed of exporting files to researchers was relatively slow.

Therefore, the present disclosure enables database conversion through a parsing process, so when the contents of an XML file are converted into a database with each tag as a column name and the sub-contents as values, if an RDBMS query statement is used, it may be omitted or missed when searching for a patient. The amount of additional information that can be imported can be extremely reduced, the speed of patient inquiry and file export to researchers can be relatively fast, and because data is managed on an RDBMS, it provides a higher level of security than storing it as a file. This is possible.

In this specification, the system for building a large-capacity database through standard 12-lead ECG data preprocessing according to the present disclosure includes various devices that can perform computational processing and provide results to the user. For example, the system for building a large-capacity database through standard 12-lead ECG data preprocessing according to the present disclosure may include both a computer and a server, or may take the form of either one.

Here, the computer may include, for example, a laptop, desktop, laptop, etc. equipped with a web browser.

The server processes information by communicating with external devices and may include a database server.

The ECG database management system of the ECG device according to the present disclosure receives an XML file of ECG data from the ECG device, classifies the XML file of the ECG data by type, and requires parsing within the XML file among the XML files classified by type. First tag containing information

Such an ECG database management system for an ECG device can construct a large-capacity database by parsing XML files generated by the ECG device, and thus can easily process large-capacity data.

Additionally, since waveform data stored in a database can be decoded and extracted, it can be easily used for actual research, etc.

Below, we will look at the ECG database management system of the ECG device in detail.

1 is a diagram showing an electrocardiogram database management system of the present disclosure. Figure 2 shows the configuration of the server of Figure 1.

Referring to Figures 1 and 2, the ECG database management system 100 may include an ECG device 110 and a server 120.

The electrocardiogram device 110 is a device that detects and amplifies minute electrical signals detected on the skin when the myocardium depolarizes with each heartbeat. During each heartbeat, a healthy heart has an ordered wave of depolarization that spreads throughout the ventricles from the signal coming from the sinoatrial node. The small voltage waveform sensed by two electrodes can appear on a screen or paper in the form of a curve.

The ECG device 110 may use two or more electrodes. And the electrodes can be paired. For example, the left arm (LA), right arm (RA), and left leg (LL) electrodes can form three pairs in the form of LA+RA, LA+LL, RA+LL, etc. The result from each pair is called a lead. Each lead is at a different angle to the heart. Different types of ECGs are recorded with different numbers of leads. For example, the electrocardiograms may be 3-lead, 5-lead or 12-lead electrocardiograms. At this time, a 12-lead ECG can record 12 different signals simultaneously. Additionally, 3-lead ECG and 5-lead ECG are constantly monitored and tend to be displayed only through an output device such as a screen.

The server 120 may include a memory 121 and a processor 122.

The memory 121 can store data for an algorithm for controlling the operation of components in the device or a program that reproduces the algorithm, and at least one device that performs the above-described operation using the data stored in the memory 121. It may be implemented with the processor 122. Here, the memory 121 and the processor 122 may each be implemented as separate chips. Additionally, the memory 121 and processor 122 may be implemented as a single chip.

The memory 121 can store data supporting various functions of the device and a program for the operation of the processor 122, can store input/output data, and can store a plurality of application programs running on the device. program or application), data and commands for operation of the device. At least some of these applications may be downloaded from an external server via wireless communication.

The memory 121 may be a flash memory type, a hard disk type, a solid state disk type, an SDD type (Silicon Disk Drive type), or a multimedia card micro type. 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), EEPROM (electrically erasable) It may include at least one type of storage medium among programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. Additionally, the memory 121 is separate from the main device, but may be a database connected by wire or wirelessly.

The memory 121 may store a data table related to management of the ECG database. The processor 122 may control operations related to management of the ECG database.

The processor 122 may receive an XML file of ECG data from the ECG device 110 and classify the XML file of ECG data by type.

The processor 122 may select first tag information containing information that needs to be parsed within the XML file among XML files classified by type, and select a second tag corresponding to the waveform type of the ECG data among the first tag information. Information can be selected. At this time, when selecting the second tag information, the processor 122 may select tag information related to 'RestingECG.Waveform' in the second tag information. At this time, parsing may be a process of selecting only the part containing the desired data within the XML file and classifying the hierarchy of syntax for database conversion.

The processor 122 may generate a data table based on the selected information, and may generate the data table based on at least one of patient information in an XML file and test information for each patient. Here, when creating a data table, the processor 122 may directly convert the second tag information encoded based on Base64 into a database.

The processor 122 can store data tables and create a database.

The processor 122 may extract waveform data from the database in an analyzable form. At this time, when decoding Base64 encoded data into original waveform data, the processor 122 may decode each waveform data recorded for each ECG electrode of the ECG device 110 for each time period.

Figure 3 is a flowchart showing the ECG database management method of the present disclosure. FIG. 4 is a diagram showing an example of a process of storing first tag information in a data table in the processor of FIG. 2.

FIG. 5 is a diagram showing an example of a process of storing second tag information in a data table in the processor of FIG. 2.

FIG. 6 is a diagram showing the results of being converted into a database by the processor of FIG. 2, and FIG. 7 is a diagram showing the results of waveform data decoding by the processor of FIG. 2.

Referring to FIG. 3, the ECG database management method includes a reception step (S310), a classification step (S320), a first tag information selection step (S330), a second tag information selection step (S340), and a data table creation step (S350). ), a databaseization step (S360), and a decoding step (S370).

In the receiving step, an XML file of ECG data may be received from the ECG device 110 through the processor 122 (S310). XML stands for Extensible Markup Language. It is a general-purpose markup language recommended by W3C, and XHTML, MathML, SVG, XUL, RSS, RDF, etc. are used based on XML. According to the grammatical rules of XML, users can arbitrarily define data content, and it is easy to express and store data. DTD stands for Document Type Definition and defines the structure of an XML document. In XML, you can set which document type definition to follow in the document type declaration.

At this time, the ECG data may be standard 12-lead ECG data. Here, the electrocardiogram data may be data obtained by acquiring and comprehensively analyzing multiple waveforms of the heart waveform projected in a specific direction. When depolarization occurs in the atrium, the atrium contracts and the electrical state within the heart changes. The vector that occurs due to changes in the electrical state within the heart is called a cardiac vector. Therefore, if the cardiac vector can be analyzed, the direction and size of the electrical waveform generated in the heart can be derived. Here, the standard 12-lead electrocardiogram data may be data that analyzes the state of the heart through cardiac vector analysis from 12 different directions. At this time, since the standard 12-lead ECG data follows the structure of a pre-written DTD file, parsing can be performed to select only the portion containing the desired data in the XML file to create a database.

In the classification step, the XML file of ECG data can be classified by type through the processor 122 (S320). For example, an XML file of ECG data may include patient information, test information for each patient, ECG information for each patient, etc.

In the first tag information selection step, the processor 122 may select first tag information containing information that needs to be parsed within the XML file among XML files classified by type (S330). For example, the first tag information may include XML files classified by type as follows.

'RestingECG.PatientDemographics.PatientID',

'RestingECG.PatientDemographics.PatientAge',

'RestingECG.PatientDemographics.Gender',

'RestingECG.TestDemographics.AcquisitionTime',

'RestingECG.TestDemographics.AcquisitionDate',

'RestingECG.RestingECGMeasurements.VentricularRate',

'RestingECG.RestingECGMeasurements.AtrialRate',

'RestingECG.RestingECGMeasurements.PRInterval',

'RestingECG.RestingECGMeasurements.QRSDuration',

'RestingECG.RestingECGMeasurements.QTInterval',

'RestingECG.RestingECGMeasurements.QTCorrected',

'RestingECG.RestingECGMeasurements.PAxis',

'RestingECG.RestingECGMeasurements.RAxis',

'RestingECG.RestingECGMeasurements.TAxis',

'RestingECG.RestingECGMeasurements.QRSCount',

'RestingECG.RestingECGMeasurements.QOnset',

'RestingECG.RestingECGMeasurements.QOffset',

'RestingECG.RestingECGMeasurements.POnset',

'RestingECG.RestingECGMeasurements.POffset',

'RestingECG.RestingECGMeasurements.TOffset',

'RestingECG.OriginalRestingECGMeasurements.ECGSampleBase',

'RestingECG.Diagnosis.DiagnosisStatement',

'RestingECG.QRSTimesTypes.QRS',

'RestingECG.QRSTimesTypes.GlobalRR',

'RestingECG.QRSTimesTypes.QTRGGR',

'RestingECG.Waveform',

In the second tag information selection step, the second tag information corresponding to the waveform type of the ECG data may be selected from the first tag information through the processor 122 (S340). At this time, when selecting the second tag information, the processor 122 may select tag information related to 'RestingECG.Waveform' in the second tag information.

Here, all tags except the 'RestingECG.Waveform' tag can have text data without child tags. At this time, the 'RestingECG.Waveform' tag has the waveform type as a sub-tag, and each sub-tag may store text data in Base64 encoded state.

For example, tag information related to 'RestingECG.Waveform' is as follows.

'RestingECG.Waveform.I'

'RestingECG.Waveform.II'

'RestingECG.Waveform.V1'

'RestingECG.Waveform.V2'

'RestingECG.Waveform.V3'

'RestingECG.Waveform.V4'

'RestingECG.Waveform.V5'

'RestingECG.Waveform.V6'

Here, I is the potential difference between the right and left hands, II is the potential difference between the right and left feet, V1 is the 4th intercostal space at the sternum margin, V2 is the 4th intercostal space at the left margin of the sternum, V3 is the midpoint of the line connecting V2 and V4 on the chest wall, V4 may be the 5th intercostal space on the mid clavicular line, V5 may be the anterior axillary line at the level of V4, and V6 may be the mid axillary line at the level of V4. At this time, 8 types of waveforms may be stored as sub-tags in the 'RestingECG.Waveform' column.

Here, when creating the data table DT1, the processor 122 may directly convert the second tag information encoded based on Base64 into a database. This Base64-based encoding and decoding process converts 8-bit binary data into text data consisting of only characters that are not affected by character codes, and decodes it into the original waveform data to prevent data loss during the transmission process. It can be a process.

For example, the processor 122 may decode each waveform data (D27 to D34) recorded for each of the 12 ECG electrodes of the ECG device 110 for each time period. In other words, the processor 122 can decode each waveform data (D27 to D34) for each of the 12 ECG electrodes in different time periods.

In addition, the processor 122 configures each of the two pairs of electrodes (the first group of the first and second ECG electrodes, the second group of the third and fourth ECG electrodes, and the third group, seventh, and group of the fifth and sixth ECG electrodes). Each waveform data (D27 to D34) for the 4th group of 8 ECG electrodes, the 5th group of 9th and 10th ECG electrodes, and the 6th group of 11th and 12th ECG electrodes) can be decoded for each different time period. . In other words, the processor 122 may decode each waveform data D27 to D34 for the first to sixth groups in different time periods.

In addition, the processor 122 configures each of the three pairs of electrodes (the first group of the 1st, 2nd, and 3rd ECG electrodes, the second group of the 4th, 5th, and 6th ECG electrodes, and the first group of the 7th, 8th, and 9th ECG electrodes). Each waveform data (D27 to D34) for 3 groups (4th group of 10th, 11th, and 12th ECG electrodes) can be decoded for each different time period. In other words, the processor 122 may decode each waveform data D27 to D34 for the first to fourth groups in different time periods.

In addition, the processor 122 configures each of the four pairs of electrodes (the first group of the 1st, 2nd, 3rd, and 4th ECG electrodes, the second group of the 5th, 6th, 7th, and 8th ECG electrodes, and the 9th, 10th, and 11th groups of the ECG electrodes). , each waveform data (D27 to D34) for the third group of 12 ECG electrodes can be decoded for each different time period. In other words, the processor 122 may decode each waveform data D27 to D34 for the first to third groups in different time periods.

Additionally, the processor 122 configures each of the six pairs of electrodes (a first group of 1st, 2, 3, 4, 5, and 6 ECG electrodes, a second group of 7th, 8, 9, 10, 11, and 12 ECG electrodes). For each group), each waveform data (D27 to D34) can be decoded for different time periods. In other words, the processor 122 may decode each waveform data (D27 to D34) for the first group and the second group in different time periods.

Meanwhile, the processor 122 may determine the recording time of each waveform data (D27 to D34) for each of the 12 ECG electrodes. The processor 122 may determine whether the recording time is in a first state that is earlier than the preset first time, determines whether the recording time is in a second state that is faster than the first time and later than the second time, and is faster than the second time. You can determine whether it is in the third state.

At this time, the processor 122 may decode the group of ECG electrodes corresponding to the third state as first priority, and may decode the group of ECG electrodes corresponding to the second state as second priority, and may decode the group of ECG electrodes corresponding to the second state as second priority. The group of ECG electrodes corresponding to can be decoded as third priority. For example, the processor 122 may decode the first group of the 1st, 3rd, 4th, and 5th ECG electrodes corresponding to the third state among the 12 ECG electrodes as first priority, and the first group of the 12 ECG electrodes corresponding to the third state. The second group of the 2nd, 6th, 10th, and 12th ECG electrodes corresponding to state 2 can be decoded as the second priority, and the 7th, 8th, 9th, and 11th ECG electrodes corresponding to the first state among the 12 ECG electrodes. The third group of can be decoded to the third rank. As such, the processor 122 may decode the first group of ECG electrodes 1, 3, 4, and 5 with the fastest recording times as the first rank, and the second, 6, and 10 groups with recording times later than the first rank. , the second group of 12 ECG electrodes can be decoded as the second rank, and the third group of the 7th, 8th, 9th, and 11th ECG electrodes whose recording time is later than the second rank can be decoded as the third rank, When decoding, it can be quickly decoded by priority group, allowing large amounts of data to be processed efficiently and quickly.

In the data table creation step, the processor 122 may generate a data table based on at least one of patient information and patient-specific test information in the XML file (S350). For example, as shown in FIG. 5, the processor 122 includes waveform data (D27 to D34) based on each metadata (D1 to D26) related to patient information and patient-specific test information, and creates a data table. (DT1) can be generated.

In the database creation step, a data table can be stored through the memory 121 and converted into a database (S360).

In the decoding step, when converting the Base64 encoded data into analyzable waveform data through the memory 121, each waveform data recorded for each ECG electrode of the ECG device can be decoded for each time period ( S370). At this time, when decoding Base64 encoded data into original waveform data, the processor 122 may decode each waveform data recorded for each ECG electrode of the ECG device 110 for each time period.

Semi-structured data, such as the XML format of the present disclosure, can be converted into structured data through a parsing process, and thus can be stored in table form in the RDBMS through the memory 121. Here, R in RDBMS stands for Relational, and RDBMS stands for relational database management system. RDBMS is a database that expresses all data in the form of a two-dimensional table based on the relational data model.

At this time, in the XML file, ECG data may be classified into tags according to type and recorded as text.

Here, the name of the tag to be parsed can be used as the column name, the sub-contents of the tag can be used as the value, time series data about the ECG can be called waveform data, and other data (patient ID, examination date, etc.) can be called metadata. there is. At this time, in the case of waveform data, Base64 encoded values, rather than text, can be recorded to prevent data loss during the transmission process.

Base64 encoding is a method of converting 8-bit binary data into text data consisting only of characters that are not affected by character codes. For example, in the Base64 encoding method, binary data can be cut into 6 bits, then the characters corresponding to 6 bits can be found and replaced in the Base64 index table. At this time, the Base64 index table may refer to a table that matches 52 upper and lower case letters, 10 single-digit numbers, spaces, and slash symbols (/) to values from 0 to 63. In this way, the results databased by the processor 122 can be expressed in a database table as shown in FIG. 7.

In the decoding step, waveform data may be decoded through the processor 122 to analyze the databased ECG data (S370). At this time, the processor 122 can search the database for data corresponding to the patient group requested by the researcher's terminal through a query statement. The processor 122 stores the searched data as a CSV file and delivers it to the researcher's terminal. The researcher's terminal can obtain the decoded waveform data for each patient as a CSV file through the decoding module.

At this time, the decoding module can additionally calculate four waveform types corresponding to [Equation 1] to [Equation 4] below from the waveform data for the lower tags.

[Equation 1]

III = II - I

[Equation 2]

aVR = I - 0.5 * II

[Equation 3]

aVL = I + 0.5 * II

[Equation 4]

aVF = II - 0.5 * I

At this time, I may be the potential difference between the right hand and the left hand, II may be the potential difference between the right hand and the left foot, III may be the potential difference between the left foot and the left hand, Avr may be the potential difference between the center of the heart and the right hand, and aVL may be the potential difference between the center of the heart and the right hand. It can be the potential difference between the center of the heart and the left hand, and aVF can be the potential difference between the center of the heart and the left foot. In this way, the result of decoding the waveform data by the processor 122 can be expressed in a decoded table as shown in FIG. 8.

Meanwhile, the computer program stored in the computer-readable storage medium, when executed on one or more processors, performs the following operations for performing the ECG database management method of the ECG device 110, and the operations include the ECG device ( It may include an operation of receiving an XML file of ECG data from 110) and an operation of classifying the XML file of ECG data by type. At this time, the ECG data may be standard 12-lead ECG data.

In addition, the above operations include selecting first tag information containing information requiring parsing within the XML file among XML files classified by type, and second tag information corresponding to the waveform type of ECG data among the first tag information. The operation of selecting tag information may be included. At this time. The second tag information selection operation may be an operation of selecting tag information related to 'RestingECG.Waveform' in the first tag information.

Additionally, the operations may include generating a data table based on at least one of patient information and patient-specific examination information in the XML file. From here,

Additionally, the operations may include creating a data table and storing the data table into a database.

Additionally, the waveform data decoding operation may be an operation of decoding each waveform data recorded for each ECG electrode of the ECG device by time slot when decoding Base64 encoded data into original waveform data.

Meanwhile, a computing device for managing the ECG database of the ECG device 110 includes a processor including one or more cores; and a memory storing instructions executable by the processor, wherein when the instructions are executed by the processor, the processor receives an XML file of the ECG data from the ECG device 110 and stores an XML file of the ECG data. Can be classified by type. At this time, the ECG data may be standard 12-lead ECG data.

The processor selects first tag information containing information that needs to be parsed within the XML file among XML files classified by type, and selects second tag information corresponding to the waveform type of the ECG data from among the first tag information. You can. At this time, when selecting the second tag information, the processor may select tag information related to 'RestingECG.Waveform' in the second tag information.

Additionally, the processor may generate a data table based on the selected information and generate the data table based on at least one of patient information and patient-specific test information in an XML file. Here, when creating a data table, the processor can directly convert the second tag information encoded based on Base64 into a database.

Additionally, the processor may create a data table, store the data table, and convert the data table into a database.

This disclosure allows building and utilizing a database for electrocardiogram data generated for observation during patient treatment in a hospital.

In addition, the present disclosure performs parsing to select only the part containing desired data within an XML document and converts it into a database after parsing, thereby designing a structure that facilitates data analysis based on tables in the database, allowing data users to analyze the data. It can be made convenient to use.

In addition, the present disclosure can build a large-capacity database by parsing XML files generated from an ECG device, so it can be utilized in the field of artificial intelligence using large-capacity ECG data.

Additionally, waveform data stored in the database of the present disclosure can be used for actual research, etc. through a separate decoding module. At this time, when decoding Base64 encoded data into original waveform data, the processor may decode each waveform data recorded for each ECG electrode of the ECG device 110 for each time period.

At least one component may be added or deleted in response to the performance of the components shown in FIGS. 1 and 2. Additionally, it will be easily understood by those skilled in the art that the mutual positions of the components may be changed in response to the performance or structure of the system.

Figure 3 shows that a plurality of steps are sequentially executed, but this is merely an illustrative explanation of the technical idea of this embodiment, and those skilled in the art will understand the essential characteristics of this embodiment. Various modifications and modifications can be made by executing by changing the order shown in FIG. 3 or executing one or more of the plurality of steps in parallel within the scope of the above, so FIG. 3 is not limited to a time-series order. .

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. Instructions may be stored in the form of program code and, when executed by a processor, may create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

Computer-readable recording media include all types of recording media storing instructions that can be decoded by a computer. For example, there may be Read Only Memory (ROM), Random Access Memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, etc.

As described above, the disclosed embodiments have been described with reference to the attached drawings. A person skilled in the art to which this disclosure pertains will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

100: ECG database management system 110: ECG device
120: Server 121: Memory
122: processor

Claims (19)

In an ECG database management method of an ECG device performed by a server,
Receiving an XML file of ECG data from the ECG device;
Classifying the XML file of the ECG data by type;
Selecting first tag information containing information that needs to be parsed within the XML file among the XML files classified by type;
Selecting second tag information corresponding to a waveform type of ECG data from among the first tag information;
Creating a data table based on at least one of patient information and patient-specific test information in the XML file;
Saving the data table and converting it into a database;
and
An electrocardiogram database management method for an electrocardiogram device, comprising extracting waveform data from the database in an analyzable form. According to paragraph 1,
The second tag information selection step is,
An ECG database management method of an ECG device, characterized in that tag information related to 'RestingECG.Waveform' in the first tag information is selected. According to paragraph 2,
The data table creation step is,
An ECG database management method for an ECG device, characterized in that the Base64 encoded data is converted into a database as is. According to paragraph 1,
The method, characterized in that the electrocardiogram data is standard 12-lead electrocardiogram data. In the server for managing the electrocardiogram database of the electrocardiogram device,
a memory storing a data table related to management of the ECG database; and
Includes a processor that controls operations related to management of the ECG database,
The processor,
Receiving an XML file of ECG data from the ECG device,
Classifies the XML files of the ECG data by type,
Among the XML files classified by type, select first tag information containing information that needs to be parsed within the XML file,
Selecting second tag information corresponding to the waveform type of ECG data from the first tag information,
Create a data table based on the selected information,
Creating a data table based on at least one of patient information and patient-specific test information in the XML file,
The data table is stored and converted into a database,
A server characterized by extracting waveform data from a database in an analyzable form. According to clause 5,
The processor,
When selecting the second tag information,
A server characterized in that it selects tag information related to 'RestingECG.Waveform' in the second tag information. According to clause 6,
The processor,
When creating the data table,
A server characterized in that the Base64 encoded data is converted into a database as is. In clause 7,
The processor,
When extracting waveform data into an analyzable form,
When converting the Base64 encoded data into analyzable waveform data,
A server, characterized in that each waveform data recorded for each ECG electrode of the ECG device is decoded by time period. According to clause 5,
The server, characterized in that the electrocardiogram data is standard 12-lead electrocardiogram data. A computer program stored in a computer-readable storage medium, wherein the computer program, when executed on one or more processors, performs the following operations for performing an ECG database management method of an ECG device,
The above operations are:
Receiving an XML file of ECG data from the ECG device;
Classifying the XML file of the ECG data by type;
An operation of selecting first tag information containing information that needs to be parsed within the XML file among the XML files classified by type;
An operation of selecting second tag information corresponding to a waveform type of ECG data from among the first tag information;
Creating a data table based on at least one of patient information and patient-specific test information in the XML file;
An operation of storing the data table and converting it into a database; and
A computer program stored in a computer-readable storage medium, comprising extracting waveform data from a database into an analyzable form. According to clause 10,
The second tag information selection operation is,
A computer program stored in a computer-readable storage medium, characterized in that selecting tag information related to 'RestingECG.Waveform' in the first tag information. According to clause 11,
The data table creation operation is,
A computer program stored in a computer-readable storage medium, characterized in that the Base64 encoded data is converted into a database as is. According to clause 12,
The data table creation operation is,
When extracting waveform data into an analyzable form,
When converting the Base64 encoded data into analyzable waveform data,
A computer program stored in a computer-readable storage medium, characterized in that each waveform data recorded for each ECG electrode of the ECG device is decoded for each time period. According to clause 10,
A computer program stored in a computer-readable storage medium, wherein the electrocardiogram data is standard 12-lead electrocardiogram data. A computing device for managing an electrocardiogram database of an electrocardiogram device,
A processor including one or more cores; and
Includes a memory that stores instructions executable by the processor,
When the instructions are executed by the processor,
The processor,
Receiving an XML file of ECG data from the ECG device,
Classifies the XML files of the ECG data by type,
Among the XML files classified by type, select first tag information containing information that needs to be parsed within the XML file,
Selecting second tag information corresponding to the waveform type of ECG data from the first tag information,
Create a data table based on the selected information,
Creating a data table based on at least one of patient information and patient-specific test information in the XML file,
The data table is stored and converted into a database,
A computing device characterized by extracting waveform data from a database into an analyzable form. According to clause 15,
The processor,
When selecting the second tag information,
A computing device characterized in that it selects tag information related to 'RestingECG.Waveform' in the second tag information. According to clause 16,
The processor,
When creating the data table,
A computing device, characterized in that the Base64 encoded data is converted into a database as is. According to clause 17,
The processor,
When extracting waveform data into an analyzable form,
When converting the Base64 encoded data into analyzable waveform data,
A computing device, characterized in that each waveform data recorded for each ECG electrode of the ECG device is decoded by time period. According to clause 15,
A computing device, characterized in that the electrocardiogram data is standard 12-lead electrocardiogram data.