Lobachevsky University Electrocardiography Database

Creators: Alena Kalyakulina, Igor Yusipov, Viktor Moskalenko, Alexander Nikolskiy, Konstantin Kosonogov, Nikolai Zolotykh, Mikhail Ivanchenko

Published: Jan. 19, 2021. Version: 1.0.1

Citation

Please cite:
Kalyakulina, A., Yusipov, I., Moskalenko, V., Nikolskiy, A., Kosonogov, K., Zolotykh, N., & Ivanchenko, M. (2021). Lobachevsky University Electrocardiography Database (version 1.0.1). PhysioNet. https://doi.org/10.13026/eegm-h675.

Additionally, cite the original publication:
Kalyakulina, A.I., et al. LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms, IEEE Access, 2020. DOI: 10.1109/ACCESS.2020.3029211

Standard PhysioNet citation:
Goldberger, A., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Circulation, 101(23), e215–e220.

Abstract

Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal database with manually annotated boundaries and peaks of P waves, T waves, and QRS complexes. The database consists of 200 10-second 12-lead ECG signal records from healthy volunteers and patients with cardiovascular diseases (some with pacemakers), collected at Nizhny Novgorod City Hospital No 5 (2017–2018).

Background

Existing databases like MIT-BIH and QT have limitations in annotation completeness. LUDB aims to provide complete, independently annotated ECGs for training, testing, and validating ECG delineation algorithms.

Methods

ECG signals were recorded with a Schiller Cardiovit AT-101 cardiograph at 500 Hz sampling rate across 12 leads. Certified cardiologists manually annotated each record. Volunteers provided informed consent; IRB approval was obtained from Lobachevsky University (#23, 19 October 2017).

Data Description

• 200 10-second, 12-lead ECGs
• 16,797 P waves, 21,966 QRS complexes, 19,666 T waves (total: 58,429 annotated waves)
• Subjects: Ages 11 to >89 years (mean 52), 85 women, 115 men

Rhythms

Electrical Axis

Conduction Abnormalities

Extrasystolies

Includes atrial and ventricular extrasystoles in various localizations and patterns. (Detailed numbers available on request.)

Hypertrophies

Cardiac Pacing

• UNIpolar atrial pacing: 1
• UNIpolar ventricular pacing: 6
• BIpolar ventricular pacing: 2
• Biventricular pacing: 1
• P-synchrony: 2

Ischemia

Various ischemia types (STEMI, NSTEMI, Scar formation) affecting anterior, lateral, septal, inferior, posterior, and apical walls, with corresponding counts (detailed list above).

Non-specific Repolarization Abnormalities

• Anterior wall: 18
• Lateral wall: 13
• Septal: 15
• Inferior wall: 19
• Posterior wall: 9
• Apical: 11

Other States

• Early repolarization syndrome: 9 ECGs

Usage Notes

The data is stored in WFDB-compatible format, usable via WFDB Software Package or WFDB Python Toolbox. LUDB has been used successfully for training/testing deep learning ECG delineation algorithms achieving up to 99.9% F1-scores for QRS complexes.

Release Notes

• Version 1.0.1: Added waveform scaling info, rhythm annotations, ludb.csv patient info file.
• Version 1.0.0: Initial release.

Acknowledgements

Supported by the Ministry of Education of the Russian Federation (contract No. 02.G25.31.0157 of 01.12.2015).

Conflicts of Interest

The authors declare no known conflicts of interest.

References

1. Kalyakulina, A.I., et al. (2019). Radiophysics and Quantum Electronics, 61(8-9), 689-703.
2. Moody, G.B., & Mark, R.G. (2001). IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50.
3. Taddei, A., et al. (1992). European Heart Journal, 13(9), 1164-1172.
4. Laguna, P., et al. (1997). Computers in cardiology, 673-676.
5. Sereda, I., et al. (2019). IJCNN Conference.
6. Bogdanov, M., et al. (2019). arXiv:1912.04672.
7. Moskalenko, V., et al. (2019). Neuroinformatics Conference.
8. Kuznetsov, V.V., et al. (2020). arXiv:2002.00254.
9. Ruiz, A., et al. (2019). EHB Conference.
10. Chen, G., et al. (2020). IEEE Access, 8, 10707-10717.