Key result
Standardized KURIAS-ECG database reduces baseline displacement vs unedited data, enabling ~88% AI classification accuracy.
Why the study?
Expert ECG diagnoses lack uniform quality, and existing public ECG databases lack a common standard allowing them to be combined for research.
The KURIAS-ECG database provides a standardized, high-quality dataset of 12-lead ECGs mapped to SNOMED CT and OMOP-CDM to facilitate cardiovascular research and AI model development.
May accelerate standardized ECG-AI development; leaves open external validation before clinical adoption.
OBJECTIVES: Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. METHODS: The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership-common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. RESULTS: The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. CONCLUSIONS: The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.
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Yoo et al. (2023) studied Cardiovascular Diseases (n=13,862). Standardized ECG database construction with poor-quality ECG removal vs. Database without poor-quality ECG removal was evaluated on Baseline displacement of ECG waveforms (p=<0.01). The standardized KURIAS-ECG database, constructed by removing poor-quality ECGs, significantly reduced baseline displacement compared to the unedited dataset (44.54 μV vs 50.33 μV, p < 0.01) and enabled an AI classification accuracy of 88.03%.
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