MIT-BIH Malignant Ventricular Ectopy 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 22 half-hour ECG recordings of subjects who experienced episodes of sustained ventricular tachycardia, ventricular flutter, and ventricular fibrillation. The reference annotation (.atr) files contain only rhythm labels (no beat labels).

Annotations in the MIT-BIH Malignant Ventricular Fibrillation Database

All of the annotations contained in the .atr files of this database are rhythm change annotations, and almost all of them mark changes in cardiac rhythm (a few indicate episodes of noise or asystole). The table below indicates how the aux strings of these annotations should be interpreted.

• AFIB: atrial fibrillation
• ASYS: asystole
• B: ventricular bigeminy
• BI: first degree heart block
• HGEA: high grade ventricular ectopic activity
• N or NSR: normal sinus rhythm
• NOD: nodal ("AV junctional") rhythm
• NOISE: noise
• PM: pacemaker (paced rhythm)
• SBR: sinus bradycardia
• SVTA: supraventricular tachyarrhythmia
• VER: ventricular escape rhythm
• VF or VFIB: ventricular fibrillation
• VFL: ventricular flutter
• VT: ventricular tachycardia

In each case, the rhythm change annotations are placed at the beginning of the episode of the indicated rhythm. The previous rhythm continues during episodes marked by (NOISE; the noise ends at the time of the next annotation.

Further reading

Greenwald SD, Albrecht P, Moody GB, Mark RG. Estimating confidence limits for arrhythmia detector performance. Computers in Cardiology 1985; 12:383–386.

Cited by

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

• A systematic review of automated prediction of sudden cardiac death using ECG signals
  Preeti P. Ghasad, Jagath V S Vegivada, Vipin Milind Kamble et al. Physiological Measurement, 2024.
• Graph Attention Networks Based Cardiac Arrest Diagnosis
  Phong Tuc Vu, Anh Nguyen, Minh Tuan Nguyen. International Conference on Advanced Technologies for Communications, 2024.
• A Hybrid GAN-Based DL Approach for the Automatic Detection of Shockable Rhythms in AED for Solving Imbalanced Data Problems
  Kamana Dahal, Mohd. Hasan Ali. Electronics, 2022.
• Overview of Machine Learning and Deep Learning Approaches for Detecting Shockable Rhythms in AED in the Absence or Presence of CPR
  Kamana Dahal, Mohd. Hasan Ali. Electronics, 2022.

View all 4 publications

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