A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients

By Jianwei Zheng, Jianming Zhang, Sidy Danioko, H. Yao, Hangyuan Guo, C. Rakovski. Scientific Data, 2020

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This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Advancement of modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 10,646 patients with a 500 Hz sampling rate that features 11 common rhythms and 67 additional cardiovascular conditions, all labeled by professional experts. The dataset consists of 10-second, 12-dimension ECGs and labels for rhythms and other conditions for each subject. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions. Measurement(s) cardiac arrhythmia Technology Type(s) 12 lead electrocardiography • digital curation Factor Type(s) sex • experimental condition • age group Sample Characteristic - Organism Homo sapiens Measurement(s) cardiac arrhythmia Technology Type(s) 12 lead electrocardiography • digital curation Factor Type(s) sex • experimental condition • age group Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11698521

Subfields: Cardiology and Cardiovascular Medicine

View Paper: https://www.semanticscholar.org/paper/2ba61722c2aaede5c920a942980e7433947ef538

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In Cardiology and Cardiovascular Medicine

  1. Dani Kiyasseh
  2. David A. Clifton
  3. T. Zhu

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