Construction of an Electrocardiogram Database Including 12 Lead Waveforms.
10.4258/hir.2018.24.3.242
- Author:
Dahee CHUNG
1
;
Junggu CHOI
;
Jong Hwan JANG
;
Tae Young KIM
;
JungHyun BYUN
;
Hojun PARK
;
Hong Seok LIM
;
Rae Woong PARK
;
Dukyong YOON
Author Information
1. Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea. d.yoon.ajou@gmail.com
- Publication Type:Case Report
- Keywords:
Electrocardiogram;
Waveform;
QT Interval;
Database;
Adverse Drug Reaction
- MeSH:
Calcium;
Cardiovascular Diseases;
Demography;
Diagnosis;
Drug-Related Side Effects and Adverse Reactions;
Electrocardiography*;
Electronic Health Records;
Hospitals, Teaching;
Korea;
Machine Learning;
Magnesium;
Potassium
- From:Healthcare Informatics Research
2018;24(3):242-246
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.
