Database Open Access

Lobachevsky University Electrocardiography Database

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

Published: Jan. 19, 2021. Version: 1.0.1

When using this resource, please cite: (show more options)
Kalyakulina, A., Yusipov, I., Moskalenko, V., Nikolskiy, A., Kosonogov, K., Zolotykh, N., & Ivanchenko, M. (2021). Lobachevsky University Electrocardiography Database (version 1.0.1). PhysioNet.

Additionally, please cite the original publication:

Kalyakulina, A.I., Yusipov, I.I., Moskalenko, V.A., Nikolskiy, A.V., Kosonogov, K.A., Osipov, G.V., Zolotykh, N.Yu., Ivanchenko, M.V.: LUDB: A New Open-Access Validation Tool for Electrocardiogram Delineation Algorithms, IEEE Access, vol. 8, pp. 186181-186190, 2020, doi: 10.1109/ACCESS.2020.3029211

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.


Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal database with marked boundaries and peaks of P, T waves and QRS complexes. The database consists of 200 10-second 12-lead ECG signal records representing different morphologies of the ECG signal. The ECGs were collected from healthy volunteers and patients of the Nizhny Novgorod City Hospital No 5 in 2017–2018. The patients had various cardiovascular diseases while some of them had pacemakers. The boundaries of P, T waves and QRS complexes were manually annotated by cardiologists for all 200 records. Also, each record is annotated with the corresponding diagnosis. The database can be used for educational purposes as well as for training and testing algorithms for ECG delineation, i.e. for automatic detection of boundaries and peaks of P, T waves and QRS complexes.


Validating ECG delineation algorithms requires standardized databases with complexes and waves, manually annotated by specialists. Several collections are currently available: MIT-BIH Arrhythmia Database [1], European ST-T Database [2], and QT Database [3], however their annotation is not exhaustive. For example, MIT-BIH Arrhythmia Database and European ST-T Database has a markup only for QRS complexes. The QT Database contains annotations for P, QRS and T waves, but several complexes are unmarked. By assembling a new ECG database at Lobachevsky University (LUDB), we sought to eliminate these shortcomings.


ECG 10 seconds records were obtained by the Schiller Cardiovit AT-101 cardiograph, with conventional 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6). Signals are digitized at 500 samples per second. The boundaries and peaks of P, T waves and QRS complexes were determined by certified cardiologists by an eye inspection of each ECG signal and independently for each of 12 leads. The records were made by specialized medical staff (functional diagnostics nurses). All volunteers provided informed written consent before collecting the data. The research was approved by Lobachevsky University IRB (#23; 19 October 2017)

Data Description

The database consists of 200 10-second 12-lead ECG signal records collected from 2017 to 2018: in total, 16797 P waves, 21966 QRS complexes, 19666 T waves (in total, 58429 annotated waves). The age of all volunteers ranged from a minimum of 11 years old to a maximum of >89 years old with an average 52 years old while the distribution by gender was 85 women and 115 men.

The number of records with specified heart rate types in the dataset:

Rhythms Number of ECGs
Sinus rhythm 143
Sinus tachycardia 4
Sinus bradycardia 25
Sinus arrhythmia 8
Irregular sinus rhythm 2
Abnormal rhythm 19

The number of records with specified types of the position of the electrical axis of the heart:

Electric axis of the heart Number of ECGs
Normal 75
Left axis deviation 66
Vertical 26
Horizontal 20
Right axis deviation 3
Undetermined 10

The number of records with specified types of conduction abnormalities:

Conduction abnormalities Number of ECGs
Sinoatrial blockade, undetermined 1
I degree AV block 10
III degree AV-block 5
Incomplete right bundle branch block 29
Incomplete left bundle branch block 6
Left anterior hemiblock 16
Complete right bundle branch block 4
Complete left bundle branch block 4
Non-specific intravintricular conduction delay 4

The numbers of records with specified types of extrasystolies:

Extrasystolies Number of ECGs
Atrial extrasystole, undetermined 2
Atrial extrasystole, low atrial 1
Atrial extrasystole, left atrial 2
Atrial extrasystole, SA-nodal extrasystole 3
Atrial extrasystole, type: single PAC 4
Atrial extrasystole, type: bigemini 1
Atrial extrasystole, type: quadrigemini 1
Atrial extrasystole, type: allorhythmic pattern 1
Ventricular extrasystole, morphology: polymorphic 2
Ventricular extrasystole, localisation: RVOT, anterior wall 3
Ventricular extrasystole, localisation: RVOT, antero-septal part 1
Ventricular extrasystole, localisation: IVS, middle part 1
Ventricular extrasystole, localisation: LVOT, LVS 2
Ventricular extrasystole, localisation: LV, undefined 1
Ventricular extrasystole, type: single PVC 6
Ventricular extrasystole, type: intercalary PVC 2
Ventricular extrasystole, type: couplet 2

The number of records with specified types of hypertrophies:

Hypertrophies Number of ECGs
Right atrial hypertrophy 1
Left atrial hypertrophy 102
Right atrial overload 17
Left atrial overload 11
Left ventricular hypertrophy 108
Right ventricular hypertrophy 3
Left ventricular overload 11

The number of records with cardiac pacing:

Cardiac pacing Number of ECGs
UNIpolar atrial pacing 1
UNIpolar ventricular pacing 6
BIpolar ventricular pacing 2
Biventricular pacing 1
P-synchrony 2

The number of records with ischemia:

Ischemia Number of ECGs
STEMI: anterior wall 8
STEMI: lateral wall 7
STEMI: septal 8
STEMI: inferior wall 1
STEMI: apical 5
Ischemia: anterior wall 5
Ischemia: lateral wall 8
Ischemia: septal 4
Ischemia: inferior wall 10
Ischemia: posterior wall 2
Ischemia: apical 6
Scar formation: lateral wall 3
Scar formation: septal 9
Scar formation: inferior wall 3
Scar formation: posterior wall 6
Scar formation: apical 5
Undefined ischemia/scar/supp.NSTEMI: anterior wall 12
Undefined ischemia/scar/supp.NSTEMI: lateral wall 16
Undefined ischemia/scar/supp.NSTEMI: septal 5
Undefined ischemia/scar/supp.NSTEMI: inferior wall 3
Undefined ischemia/scar/supp.NSTEMI: posterior wall 4
Undefined ischemia/scar/supp.NSTEMI: apical 11

The number of records with non-specific repolarization abnormalities:

Non-specific repolarization abnormalities Number of ECGs
Anterior wall 18
Lateral wall 13
Septal 15
Inferior wall 19
Posterior wall 9
Apical 11

The number of records with other cases:

Other states Number of ECGs
Early repolarization syndrome 9

Usage Notes

The data is stored in wfdb-compatible format, which is supported by WFDB Python Toolbox or WFDB Software Package

The database was successfully used in testing and training algorithms for ECG delineation [4-10]. One of the most accurate ECG delineation algorithms [7] uses LUDB as the training set while the algorithm is based on a deep learning approach. F1-measures for detecting the beginnings and the ends of P and T waves and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.

Release Notes

1.0.1 Corrected information about scaling of the waveforms. Added information about rhythms to header files. Added ludb.csv file with aggregated information about the patients (gender, age, rhythm type, direction of the electrical axis of the heart, the presence of a cardiac pacemaker, etc.).

1.0.0 Initial release of the dataset.


The study was 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 that there are no known conflicts of interest.


  1. Kalyakulina, A. I., Yusipov, I. I., Moskalenko, V. A., Nikolskiy, A. V., Kozlov, A. A., Zolotykh, N. Y., & Ivanchenko, M. V. (2019). Finding morphology points of electrocardiographic-signal waves using wavelet analysis. Radiophysics and Quantum Electronics, 61(8-9), 689-703.
  2. Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50.
  3. Taddei, A., Distante, G., Emdin, M., Pisani, P., Moody, G. B., Zeelenberg, C., & Marchesi, C. (1992). The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European heart journal, 13(9), 1164-1172.
  4. Laguna, P., Mark, R. G., Goldberg, A., & Moody, G. B. (1997, September). A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In Computers in cardiology 1997 (pp. 673-676). IEEE.
  5. Sereda, I., Alekseev, S., Koneva, A., Kataev, R., & Osipov, G. (2019, July). ECG segmentation by neural networks: errors and correction. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
  6. Bogdanov, M., Baigildin, S., Fabarisova, A., Ushenin, K., & Solovyova, O. (2019). Effects of lead position, cardiac rhythm variation and drug-induced QT prolongation on performance of machine learning methods for ECG processing. arXiv preprint arXiv:1912.04672.
  7. Moskalenko, V., Zolotykh, N., & Osipov, G. (2019, October). Deep Learning for ECG Segmentation. In International Conference on Neuroinformatics (pp. 246-254). Springer, Cham.
  8. Kuznetsov, V. V., Moskalenko, V. A., & Zolotykh, N. Y. (2020). Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder. arXiv preprint arXiv:2002.00254.
  9. Ruiz, A., Arias, M. A., Pachón, M. I., Langley, P., Rieta, J. J., & Alcaraz, R. (2019, November). Thorough Assessment of a P-wave Delineation Algorithm Through the Use of Diverse Electrocardiographic Databases. In 2019 E-Health and Bioengineering Conference (EHB) (pp. 1-4). IEEE.
  10. Chen, G., Chen, M., Zhang, J., Zhang, L., & Pang, C. (2020). A Crucial Wave Detection and Delineation Method for Twelve-Lead ECG Signals. IEEE Access, 8, 10707-10717.


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