MIT-BIH ST Change Database

@article{pollard_physionet_2026, title = {{PhysioNet} as a global platform for biomedical research}, volume = {1}, issn = {3005-0693}, url = {https://doi.org/10.1038/s44360-026-00096-z}, doi = {10.1038/s44360-026-00096-z}, abstract = {Twenty-five years after its first public repository was established, the PhysioNet resource for biomedical and clinical data has enabled tens of thousands of research papers. Further efforts are ongoing to advance secure data sharing, community engagement and scalable research infrastructure.}, number = {8}, journal = {Nature Health}, author = {Pollard, Tom and Moody, Benjamin E. and Lehman, Li-wei H. and Gow, Brian J. and Fernandes, Chrystinne and Xie, Chen and Johnson, Alistair and Mark, Roger G. and Heldt, Thomas}, month = aug, year = {2026}, pages = {792--795},}

Data Description

This database includes 28 ECG recordings of varying lengths, most of which were recorded during exercise stress tests and which exhibit transient ST depression. The last five records (323 through 327) are excerpts of long-term ECG recordings and exhibit ST elevation.

Note that the annotation files contain only beat labels; they do not include ST change annotations, as in the European ST-T Database.

Cited by

2 publications cite this project. The most recent are listed below.

• Comprehensive survey of computational ECG analysis: Databases, methods and applications
  Elena Merdjanovska, Aleksandra Rashkovska. Expert Systems with Applications, 2022.
• Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning
  Jong-Rul Park, Sung Phil Chung, Sung Yeon Hwang et al. BMC Medical Informatics and Decision Making, 2020.

View all 2 publications

Citation data from OpenAlex. Papers that use the project without citing it may be missing.