ECG-ID 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

The database contains 310 ECG recordings, obtained from 90 persons. Each recording contains:

• ECG lead I, recorded for 20 seconds, digitized at 500 Hz with 12-bit resolution over a nominal ±10 mV range;
• 10 annotated beats (unaudited R- and T-wave peaks annotations from an automated detector);
• information (in the .hea file for the record) containing age, gender and recording date.

The records were obtained from volunteers (44 men and 46 women aged from 13 to 75 years who were students, colleagues, and friends of the author). The number of records for each person varies from 2 (collected during one day) to 20 (collected periodically over 6 months).

The raw ECG signals are rather noisy and contain both high and low frequency noise components. Each record includes both raw and filtered signals:

• Signal 0: ECG I (raw signal)
• Signal 1: ECG I filtered (filtered signal)

Contributors

This database was created and contributed by Tatiana Lugovaya, who used it in her master's thesis

Cited by

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

• Learning Cardiac Features: ECG Biometrics Across Time and Exercise
  Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine et al. Lecture notes in computer science, 2026.
• An Effective Preprocessing Data on Performance of Machine Learning for ECG-Based Personal Authentication
  Dodon Turianto Nugrahadi, Mohammad Reza Faisal, Rudy Herteno et al. International Conference on Sustainable Information Engineering and Technology (SIET), 2023.
• A novel continuous authentication method using biometrics for IOT devices
  Dipen Bhuva, Sathish Kumar. Internet of Things, 2023.
• Identificação Biométrica A Partir De ECG De Derivação Única Utilizando Perceptron Multicamadas e Transformada de Stockwell
  A. Barbosa, Pedro P. Rebouças Filho. Congresso Brasileiro de Automática, 2022.
• ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks
  Caterina Fuster-Barceló, Pedro Peris Lopez, Carmen Cámara. Neurocomputing, 2022.

View all 10 publications

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