Database Open Access
Lobachevsky University Electrocardiography Database
Published: June 4, 2020. Version: 1.0.0 <View latest version>
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Kalyakulina, A., Yusipov, I., Moskalenko, V., Nikolskiy, A., Kozlov, A., Kosonogov, K., Zolotykh, N., & Ivanchenko, M. (2020). Lobachevsky University Electrocardiography Database (version 1.0.0). PhysioNet. https://doi.org/10.13026/qweb-sr17.
Kalyakulina, A.I., Yusipov, I.I., Moskalenko, V.A., Nikolskiy, A.V., Kozlov, A.A., Kosonogov, K.A., Zolotykh, N.Yu., Ivanchenko, M.V.: LU electrocardio-graphy database: a new open-access validation tool for delineation algorithms
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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 , European ST-T Database , and QT Database , 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).
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|
|Irregular sinus rhythm||2|
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|
|Left axis deviation||66|
|Right axis deviation||3|
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|
The number of records with ischemia:
|Ischemia||Number of ECGs|
|STEMI: anterior wall||8|
|STEMI: lateral wall||7|
|STEMI: inferior wall||1|
|Ischemia: anterior wall||5|
|Ischemia: lateral wall||8|
|Ischemia: inferior wall||10|
|Ischemia: posterior wall||2|
|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|
The number of records with other cases:
|Other states||Number of ECGs|
|Early repolarization syndrome||9|
The data is stored in wfdb-compatible format, which is supported by the 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  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.
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.
- 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.
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- 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.
- 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.
- 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.
- 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.
- Moskalenko, V., Zolotykh, N., & Osipov, G. (2019, October). Deep Learning for ECG Segmentation. In International Conference on Neuroinformatics (pp. 246-254). Springer, Cham.
- Kuznetsov, V. V., Moskalenko, V. A., & Zolotykh, N. Y. (2020). Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder. arXiv preprint arXiv:2002.00254.
- 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.
- 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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Open Data Commons Attribution License v1.0
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