<div class="sc-cmRAlD dkqmWS"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-jgvlka jFuPjz"><div class="sc-gzqKSP tNtjD"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><h1>ECG IMAGES</h1>

<p>This dataset is composed of Electrocardiogram (ECG) images obtained from the database <a rel="noreferrer nofollow" aria-label="MIT-BIH Arrhythmia (opens in a new tab)" target="_blank" href="https://physionet.org/content/mitdb/1.0.0/">MIT-BIH Arrhythmia</a>. For that, the ECG signals were pre-processed, generating 109.445 images with a resolution of 256x256. In sequence, five cardiac arrhythmia superclasses recommended by <strong>AAMI</strong> were selected for work.</p>

<h3>ECG images dataset:</h3>

<p>Number of Samples: 109445<br>

Number of Categories: 5<br>

Image Resolution: 256x256<br>

Data Source: Physionet's MIT-BIH Arrhythmia Dataset<br>

Classes: [N, S, V, F, Q]</p>

<table>

<thead>

<tr>

<th>Classes</th>

<th>Images</th>

</tr>

</thead>

<tbody>

<tr>

<td>N (Normal beat)</td>

<td>90.589</td>

</tr>

<tr>

<td>S  (Supraventricular ectopic beat)</td>

<td>2.779</td>

</tr>

<tr>

<td>V (Ventricular ectopic beat)</td>

<td>7.236</td>

</tr>

<tr>

<td>F (Fusion beat)</td>

<td>803</td>

</tr>

<tr>

<td>Q (Unknown beat)</td>

<td>8.038</td>

</tr>

</tbody>

</table>

<p>More information about the acquisition of this dataset can be found at: <a rel="noreferrer nofollow" aria-label="https://github.com/analiviafr/arrhythmia_classifier (opens in a new tab)" target="_blank" href="https://github.com/analiviafr/arrhythmia_classifier">https://github.com/analiviafr/arrhythmia_classifier</a></p></div></div></div>