MIT-BIH Noise Stress Test 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},}

Abstract

This database includes 12 half-hour ECG recordings and 3 half-hour recordings of noise typical in ambulatory ECG recordings. The noise recordings were made using physically active volunteers and standard ECG recorders, leads, and electrodes; the electrodes were placed on the limbs in positions in which the subjects’ ECGs were not visible.

Data Description

The three noise records were assembled from the recordings by selecting intervals that contained predominantly baseline wander (in record 'bw'), muscle (EMG) artifact (in record 'ma'), and electrode motion artifact (in record 'em'). Electrode motion artifact is generally considered the most troublesome, since it can mimic the appearance of ectopic beats and cannot be removed easily by simple filters, as can noise of other types.

The ECG recordings were created by the script nstdbgen- using two clean recordings (118 and 119) from the MIT-BIH Arrhythmia Database, to which calibrated amounts of noise from record 'em' were added using nst. (The process of making such records is now simpler; the simplified script nstdbgen can be used with current versions of the WFDB software package to recreate these records.) Noise was added beginning after the first 5 minutes of each record, during two-minute segments alternating with two-minute clean segments. The signal-to-noise ratios (SNRs) during the noisy segments of these records are:

Since the original ECG recordings are clean, the correct beat annotations are known even when the noise makes the recordings visually unreadable. The reference annotations for these records are simply copies of those for the original clean ECGs.

Cited by

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

• Optimized adaptive filtering using genetic algorithm and signal quality index for practical motion artefact reduction in ambulatory ECG
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  Pengpeng Xie, Wenshan Zhao. Expert Systems with Applications, 2025.
• Detection of ECG Noise Using Optimized Pan-Tompkins Algorithm Combined with Squeeze-UNet
  Xuan-Dung Nguyen, An-Dong Bui, Hoang-Dat Tran. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2025.
• Lightweight 2D Squeeze-U-Net Model for Real-Time Noise Detection on Wearable ECG Device
  An-Dong Bui, Xuan-Dung Nguyen, Hoang-Dat Tran. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2025.
• Mind your indices! Index hijacking attacks on collaborative unpooling autoencoder systems
  Ki‐Chang Lee, Jonghyuk Yun, Jaeho Jin et al. Internet of Things, 2024.

View all 82 publications

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