<h1>Lobachevsky University Electrocardiography Database</h1>

<p><strong>Creators:</strong> Alena Kalyakulina, Igor Yusipov, Viktor Moskalenko, Alexander Nikolskiy, Konstantin Kosonogov, Nikolai Zolotykh, Mikhail Ivanchenko</p>

<p><strong>Published:</strong> Jan. 19, 2021. <strong>Version:</strong> 1.0.1</p>

<h2>Citation</h2>

<p>Please cite:<br>

Kalyakulina, A., Yusipov, I., Moskalenko, V., Nikolskiy, A., Kosonogov, K., Zolotykh, N., & Ivanchenko, M. (2021). Lobachevsky University Electrocardiography Database (version 1.0.1). PhysioNet. <a href="https://doi.org/10.13026/eegm-h675">https://doi.org/10.13026/eegm-h675</a>.</p>

<p>Additionally, cite the original publication:<br>

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</p>

<p>Standard PhysioNet citation:<br>

Goldberger, A., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Circulation, 101(23), e215–e220.</p>

<h2>Abstract</h2>

<p>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).</p>

<h2>Background</h2>

<p>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.</p>

<h2>Methods</h2>

<p>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).</p>

<h2>Data Description</h2>

<ul>

<li>200 10-second, 12-lead ECGs</li>

<li>16,797 P waves, 21,966 QRS complexes, 19,666 T waves (total: 58,429 annotated waves)</li>

<li>Subjects: Ages 11 to >89 years (mean 52), 85 women, 115 men</li>

</ul>

<h3>Rhythms</h3>

<table border="1">

<tr><th>Rhythm</th><th>Number of ECGs</th></tr>

<tr><td>Sinus rhythm</td><td>143</td></tr>

<tr><td>Sinus tachycardia</td><td>4</td></tr>

<tr><td>Sinus bradycardia</td><td>25</td></tr>

<tr><td>Sinus arrhythmia</td><td>8</td></tr>

<tr><td>Irregular sinus rhythm</td><td>2</td></tr>

<tr><td>Abnormal rhythm</td><td>19</td></tr>

</table>

<h3>Electrical Axis</h3>

<table border="1">

<tr><th>Axis</th><th>Number of ECGs</th></tr>

<tr><td>Normal</td><td>75</td></tr>

<tr><td>Left axis deviation</td><td>66</td></tr>

<tr><td>Vertical</td><td>26</td></tr>

<tr><td>Horizontal</td><td>20</td></tr>

<tr><td>Right axis deviation</td><td>3</td></tr>

<tr><td>Undetermined</td><td>10</td></tr>

</table>

<h3>Conduction Abnormalities</h3>

<table border="1">

<tr><th>Abnormality</th><th>Number of ECGs</th></tr>

<tr><td>Sinoatrial blockade, undetermined</td><td>1</td></tr>

<tr><td>I degree AV block</td><td>10</td></tr>

<tr><td>III degree AV-block</td><td>5</td></tr>

<tr><td>Incomplete right bundle branch block</td><td>29</td></tr>

<tr><td>Incomplete left bundle branch block</td><td>6</td></tr>

<tr><td>Left anterior hemiblock</td><td>16</td></tr>

<tr><td>Complete right bundle branch block</td><td>4</td></tr>

<tr><td>Complete left bundle branch block</td><td>4</td></tr>

<tr><td>Non-specific intraventricular conduction delay</td><td>4</td></tr>

</table>

<h3>Extrasystolies</h3>

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

<h3>Hypertrophies</h3>

<table border="1">

<tr><th>Hypertrophy Type</th><th>Number of ECGs</th></tr>

<tr><td>Right atrial hypertrophy</td><td>1</td></tr>

<tr><td>Left atrial hypertrophy</td><td>102</td></tr>

<tr><td>Right atrial overload</td><td>17</td></tr>

<tr><td>Left atrial overload</td><td>11</td></tr>

<tr><td>Left ventricular hypertrophy</td><td>108</td></tr>

<tr><td>Right ventricular hypertrophy</td><td>3</td></tr>

<tr><td>Left ventricular overload</td><td>11</td></tr>

</table>

<h3>Cardiac Pacing</h3>

<ul>

<li>UNIpolar atrial pacing: 1</li>

<li>UNIpolar ventricular pacing: 6</li>

<li>BIpolar ventricular pacing: 2</li>

<li>Biventricular pacing: 1</li>

<li>P-synchrony: 2</li>

</ul>

<h3>Ischemia</h3>

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

<h3>Non-specific Repolarization Abnormalities</h3>

<ul>

<li>Anterior wall: 18</li>

<li>Lateral wall: 13</li>

<li>Septal: 15</li>

<li>Inferior wall: 19</li>

<li>Posterior wall: 9</li>

<li>Apical: 11</li>

</ul>

<h3>Other States</h3>

<ul>

<li>Early repolarization syndrome: 9 ECGs</li>

</ul>

<h2>Usage Notes</h2>

<p>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.</p>

<h2>Release Notes</h2>

<ul>

<li><strong>Version 1.0.1:</strong> Added waveform scaling info, rhythm annotations, ludb.csv patient info file.</li>

<li><strong>Version 1.0.0:</strong> Initial release.</li>

</ul>

<h2>Acknowledgements</h2>

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

<h2>Conflicts of Interest</h2>

<p>The authors declare no known conflicts of interest.</p>

<h2>References</h2>

<ol>

<li>Kalyakulina, A.I., et al. (2019). Radiophysics and Quantum Electronics, 61(8-9), 689-703.</li>

<li>Moody, G.B., & Mark, R.G. (2001). IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50.</li>

<li>Taddei, A., et al. (1992). European Heart Journal, 13(9), 1164-1172.</li>

<li>Laguna, P., et al. (1997). Computers in cardiology, 673-676.</li>

<li>Sereda, I., et al. (2019). IJCNN Conference.</li>

<li>Bogdanov, M., et al. (2019). arXiv:1912.04672.</li>

<li>Moskalenko, V., et al. (2019). Neuroinformatics Conference.</li>

<li>Kuznetsov, V.V., et al. (2020). arXiv:2002.00254.</li>

<li>Ruiz, A., et al. (2019). EHB Conference.</li>

<li>Chen, G., et al. (2020). IEEE Access, 8, 10707-10717.</li>

</ol>