Electrocardiograms (ECGs) are the most basic test used to diagnose or screen cardiac diseases [1–3]. Many studies have recently been conducted to advance the pre-processing and diagnostic algorithms of ECG signals using artificial intelligence (AI) and deep learning technologies. For these studies, accurate and consistent annotations of ECG diagnosis and classification, as well as sufficient and high-quality ECG data of various ECG diagnoses and classifications, are very important [4,5]. Several ECG databases (DBs) have been introduced, and recently published datasets contain many more ECG data than earlier datasets [6–17] (Table 1). Despite the existing massive ECG DBs, ECG diagnosis and classification are not standardized, and their distributions are skewed. For this reason, the development of algorithms combining various ECG DBs is limited. Moreover, because the data capacity of a 12-lead ECG is very considerable, it is important to construct an efficient dataset that can be effectively studied by researchers who may have limited infrastructure.

Prior datasets used their own ECG diagnoses and classifications. In most small ECG datasets, there are five to 10 diagnostic labels. Zheng et al. [16] recently released a large ECG dataset consisting of 10,646 ECGs annotated with 11 cardiac rhythms and 56 cardiovascular conditions redefined through human labeling. The PTB-XL dataset contains 71 ECG statements in accordance with the SCP-ECG standard [17] and also provides cross-references for other ECG annotation systems, including an ECG REFID identifier, CDISC code, and DICOM code. These annotation systems focus more on data operations, such as data transmission and storage, than on suitability for use by computational processing. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), which was adopted for the present dataset, has gained popularity as a global standard terminology system to improve interoperability by covering all areas of the medical field [18]. In the United States, the Health IT Standards Committee has recommended using SNOMED CT for health information exchange. Several medical clinical DBs have recently been built using SNOMED CT, which makes it easier to construct a DB that can be merged with other clinical data [19].

Modern ECG machines generate digitized waveform data, computerized ECG parameter measurements, and diagnostic statements. The computerized interpretation algorithms of each ECG machine vendor adopt standard ECG measurement and diagnosis classification systems, such as the Minnesota code manual [20]. Although verification by experts is the gold-standard method to confirm ECG diagnoses, human validation cannot avoid intra- and inter-observer variability, and maintaining data consistency has been noted as a challenge [4]. Automated ECG interpretation is often used in studies that construct big data sets, such as large populationbased cohort studies. Moreover, previous studies have shown that the performance of recent computerized ECG interpretations is comparable to that of expert physicians, with correct classification percentages of 91.3% for the computer program and 96.0% for cardiologists, respectively [21,22]. ECG statements in existing ECG datasets, which are mostly relatively small, were labeled by physicians. The PTB-XL dataset, which is the largest ECG dataset, contains a mixture of ECG statements labeled by physicians and ECG statements automatically interpreted by an ECG machine.

The purpose of this study was to construct a high-quality, well-defined, and evenly distributed ECG dataset of sufficient size for research compiled into a practically usable DB. In this study, a pair of strategies were used to construct a high-quality DB; the first sought to establish a standardized system using standard vocabularies and Concept_IDs of SNOMED CT and Observational Medical Outcomes Partnership–common data model (OMOP-CDM) to overcome differences in the diagnoses made by different devices, while the second pursued the removal of poor-quality ECGs to prevent unnecessary data from being included in the DB. Various types of noise were also removed through a denoising process. Through this approach, 147 detailed ECG diagnoses were classified into 10 categories, and a DB containing at least 2,000 ECG data points for each category was constructed.

The established DB consisted of 13,862 patients, including 7,840 men and 6,022 women. Detailed characteristics of the patients and ECGs are shown in Table 4. Most of the ECG signals were measured using GE (88.43%). The average number of Minnesota code categories per ECG test was 2 ± 1.05, and the average number of ECG diagnoses per ECG test was 3 ± 1.49. The three most-common ECG diagnoses within each Minnesota code category are presented in Table 4. The ECG diagnosis within each category was less skewed than that in the original dataset. In addition, ECG diagnoses with a low diagnostic rate, such as indeterminate axis, were also included in a relatively high proportion.

The overall distribution of the constructed DB is shown in Figure 3. Since the same number of ECG data points were selected for each Minnesota code classification, the data-composition ratio of ECG diagnoses with low frequency, such as QRS axis deviation, increased. Thus, the distribution of ECG diagnoses for the 10 Minnesota categories was less skewed than the original data for abnormalities classified as normal, minor, and major.

The KURIAS-ECG DB represents a new type of DB constructed by standardizing various types of 12-lead ECGs and applying a method to extract high-quality data. To construct the KURIAS-ECG DB, a total of 434,938 12-lead ECGs acquired over 4 years at a general hospital were used. The KURIAS-ECG DB overcomes the differences in diagnostic information among devices by establishing a common management standard using Concept_ID. In addition, this DB is balanced by subdividing diagnostic information from 147 ECG diagnoses into 10 categories using the Minnesota classification. As research on cardiac disease using AI is carried out, the importance of ECG waveforms is growing. In previous studies, AI models were developed to classify cardiovascular diseases, such as atrial fibrillation, arrhythmias, and heart failure based on ECG waveforms, and showed an accuracy of 85%–95% [29]. Moreover, Yoo et al. [30] applied ECG waveforms to an AI model to classify neurological diseases, such as Parkinson disease, that are accompanied by changes in cardiac movement. The Parkinson disease classification model using ECG waveforms achieved an 87% accuracy for clinical patients, indicating its potential for future clinical application. Since the ECG waveform contains important information on the function of the heart, it is medical data that can be effectively used in AI research on heart-related diseases [29]. A 12-lead ECG measures the heart’s electrical activity by attaching 10 electrodes to the limbs and chest. The types of noise that occur during this process include AC interference, muscle tremors, baseline wander, and motion artifacts. As this noise is not generated by the heart function, it can reduce the accuracy of the AI model. In addition, commercial 12-lead ECG machines used in medical institutions have different diagnosis systems provided by the machine depending on the manufacturer; for example, GE refers to normal signals as “normal sinus rhythm” or “normal ECG,” while Philips uses the term “sinus rhythm.” These differences cause difficulties in extracting data in AI research that requires the use of big data, and these challenges must be resolved during the DB construction step. The KURIAS-ECG DB presented in this study applied a standardization strategy using the OMOP-CDM international standard and a high-quality strategy to exclude poor-quality ECGs containing noise. This systematic DB construction process is effective for cardiovascular disease AI research because it can obtain large amounts of data while excluding unsuitable data. ECG waveforms stored in a tree-type XML format can be difficult to use directly in research due to their data format. However, the KURIAS-ECG approach in this study extracts and manages tree-type data in cell units, making it efficient in terms of storage space utilization and easy to convert to 5,000 frames, which correspond to the original format. In the future, this approach will be a solution that can combine distributed ECG data and public DBs into a common DB through Concept_ID assignment, which is used as the standard system in KURIAS-ECG. This is expected to provide an environment for promoting research on cardiac disease using AI. The KURIAS-ECG DB is meaningful in that it is a high-quality DB of 12-lead ECGs, but there are limitations in its usability, since it was released as a limited DB on an open data platform. Since this problem is shaped by the disclosure policies of the organization providing the data, various hurdles must be overcome to construct an open DB. Therefore, in this study, the construction process of a standardization DB and related code were disclosed so that each institution can build a standardized DB without disclosing data. The construction protocol of the KURIAS-ECG DB was published on PhysioNet, and the code used for database management system (DBMS) construction and noise removal was shared through GitHub (https://github.com/KURIAS).