health life sciences medicine
This newly inaugurated research database for 12-lead electrocardiogram (ECG) signals was created under the auspices of Chapman University, Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine), and Ningbo First Hospital. It 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, ECG 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. 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 45,152 patients with a 500 Hz sampling rate that features multiple common rhythms and additional cardiovascular conditions, all labeled by professional experts. 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.
Not updated
Creative Commons Attribution 4.0 International Public License
https://doi.org/10.13026/wgex-er52
See all datasets managed by PhysioNet.
https://physionet.org/about/#contact_us
A large scale 12-lead electrocardiogram database for arrhythmia study was accessed on DATE from https://registry.opendata.aws/ecg-arrhythmia.
arn:aws:s3:::physionet-open/ecg-arrhythmia/us-east-1aws s3 ls --no-sign-request s3://physionet-open/ecg-arrhythmia/