Back to Search
Start Over
A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients
- Source :
- Scientific Data, Vol 7, Iss 1, Pp 1-8 (2020), Scientific Data
- Publication Year :
- 2020
- Publisher :
- Nature Publishing Group, 2020.
-
Abstract
- 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.<br />Measurement(s)cardiac arrhythmiaTechnology Type(s)12 lead electrocardiography • digital curationFactor Type(s)sex • experimental condition • age groupSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11698521
- Subjects :
- Statistics and Probability
Data Descriptor
Databases, Factual
Computer science
media_common.quotation_subject
MEDLINE
12 lead electrocardiogram
02 engineering and technology
030204 cardiovascular system & hematology
Library and Information Sciences
computer.software_genre
Education
Machine Learning
03 medical and health sciences
Electrocardiography
0302 clinical medicine
Quality of life (healthcare)
Physical examination
0202 electrical engineering, electronic engineering, information engineering
Humans
Quality (business)
lcsh:Science
media_common
Database
Statistics
Subject (documents)
Scientific data
Arrhythmias, Cardiac
Atrial fibrillation
Computer Science Applications
Test (assessment)
Metadata
Ecg monitoring
020201 artificial intelligence & image processing
lcsh:Q
Statistics, Probability and Uncertainty
computer
Biomedical engineering
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Data
- Accession number :
- edsair.doi.dedup.....25af0328b65b9498a813c48464624473