Construction of an Electrocardiogram Database Including 12 Lead Waveforms

Abstract

ObjectivesElectrocardiogram (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.MethodsAll 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).ResultsOur 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.ConclusionsElectrocardiogram 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.

Risk of QT prolongation through drug interactions between hydroxychloroquine and concomitant drugs prescribed in real world practice

“…QTc values from the same observational period were extracted from the local ECG repository in the MUSE system 12 , 13 . The ECG report typically contains both alphanumeric values and waveform graphs.…”

Risk Evaluation of Azithromycin-Induced QT Prolongation in Real-World Practice

“…The ECG report typically contains both alphanumeric values and waveform graphs. The QTc data were extracted by parsing alphanumeric data from the PDF data extracted from the ECG repository by web-scraping technique [ 17 ].…”

Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

“…In some cases, signal modification caused by cardiovascular system diseases might be quite similar to those affected by noise or artifacts (for example, atrial fibrillation). Therefore, we conducted ad hoc analysis by applying the proposed model to the results of portable ECGs that include interpretation by the ECG machine [23]. When our model was applied to the randomly selected 10,000 ECG lead II data, 3,337 ECGs were classified as unacceptable.…”

“…Recent ECG-related research has required a substantially different approach to noise because the scale of data collection has become very large. The Electrocardiogram Vigilance with Electronic data Warehouse II (ECG-ViEW II) released 979,273 ECG results from 461,178 patients, with plans to add all 12-lead data in the next version [2223]. More recent attempts have, additionally, been made to acquire ECG measurements from patient monitoring equipment in intensive care units (ICUs) [2425].…”