<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 ktvwwo"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><h3>Sources:</h3>

<p>(a) Original owners od Database:<br>

       -- 1. H. Altay Guvenir, PhD., <br>

             Bilkent University,<br>

             Department of Computer Engineering and Information Science,<br>

             06533 Ankara, Turkey<br>

             Phone: +90 (312) 266 4133<br>

             Email: <a rel="noreferrer nofollow" href="mailto:guvenir@cs.bilkent.edu.tr">guvenir@cs.bilkent.edu.tr</a></p>

<pre class="uc-code-block"><code>   -- <span class="hljs-number">2</span>. Burak Acar, M<span class="hljs-selector-class">.S</span>.,

         Bilkent University, 

         EE Eng. Dept. 

         <span class="hljs-number">06533</span> Ankara, Turkey

         Email: buraka@ee<span class="hljs-selector-class">.bilkent</span><span class="hljs-selector-class">.edu</span><span class="hljs-selector-class">.tr</span>

   -- <span class="hljs-number">2</span>. Haldun Muderrisoglu, M<span class="hljs-selector-class">.D</span>., Ph<span class="hljs-selector-class">.D</span>., 

         Baskent University, 

         School of Medicine

         Ankara, Turkey

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<p>(b) Donor: H. Altay Guvenir<br>

              Bilkent University,<br>

              Department of Computer Engineering and Information Science,<br>

              06533 Ankara, Turkey<br>

              Phone: +90 (312) 266 4133<br>

              Email: <a rel="noreferrer nofollow" href="mailto:guvenir@cs.bilkent.edu.tr">guvenir@cs.bilkent.edu.tr</a></p>

<p>(c) Date: January, 1998</p>

<h3>Past Usage:</h3>

<ol>

<li><p>H. Altay Guvenir, Burak Acar, Gulsen Demiroz, Ayhan Cekin<br>

  "A Supervised Machine Learning Algorithm for Arrhythmia Analysis"<br>

  Proceedings of the Computers in Cardiology Conference, <br>

  Lund, Sweden, 1997.</p>

<p>The aim is to determine the type of arrhythmia from <br>

  the ECG recordings.</p></li>

</ol>

<h3>Relevant Information:</h3>

<p>This database contains 279 attributes, 206 of which are linear<br>

     valued and the rest are nominal. </p>

<pre class="uc-code-block"><code> <span class="hljs-type">Concerning</span> the study of <span class="hljs-type">H</span>. <span class="hljs-type">Altay</span> <span class="hljs-type">Guvenir</span>: <span class="hljs-comment">"The aim is to distinguish

 between the presence and absence of cardiac arrhythmia and to

 classify it in one of the 16 groups. Class 01 refers to 'normal'

 ECG classes 02 to 15 refers to different classes of arrhythmia

 and class 16 refers to the rest of unclassified ones. For the

 time being, there exists a computer program that makes such a

 classification. However there are differences between the

 cardiolog's and the programs classification. Taking the

 cardiolog's as a gold standard we aim to minimise this difference

 by means of machine learning tools."</span>

 <span class="hljs-type">The</span> names and id numbers of the patients were recently 

 removed from the database.

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<h3>Number of Instances: 452</h3>

<h3>Number of Attributes: 279</h3>

<h3>Class Distribution:</h3>

<pre class="uc-code-block"><code>   <span class="hljs-attribute">Database</span>:  Arrhythmia

   <span class="hljs-attribute">Class</span> code :   Class   :                       Number of instances:

   <span class="hljs-attribute">01</span>             Normal                          <span class="hljs-number">245</span>

   <span class="hljs-attribute">02</span>             Ischemic changes (Coronary Artery Disease)   <span class="hljs-number">44</span>

   <span class="hljs-attribute">03</span>             Old Anterior Myocardial Infarction           <span class="hljs-number">15</span>

   <span class="hljs-attribute">04</span>             Old Inferior Myocardial Infarction           <span class="hljs-number">15</span>

   <span class="hljs-attribute">05</span>             Sinus tachycardy                       <span class="hljs-number">13</span>

   <span class="hljs-attribute">06</span>             Sinus bradycardy                       <span class="hljs-number">25</span>

   <span class="hljs-attribute">07</span>             Ventricular Premature Contraction (PVC)       <span class="hljs-number">3</span>

   <span class="hljs-attribute">08</span>             Supraventricular Premature Contraction        <span class="hljs-number">2</span>

   <span class="hljs-attribute">09</span>             Left bundle branch block                     <span class="hljs-number">9</span>   

   <span class="hljs-attribute">10</span>             Right bundle branch block                   <span class="hljs-number">50</span>

   <span class="hljs-attribute">11</span>             <span class="hljs-number">1</span>. degree AtrioVentricular block                <span class="hljs-number">0</span>   

   <span class="hljs-attribute">12</span>             <span class="hljs-number">2</span>. degree AV block                    <span class="hljs-number">0</span>

   <span class="hljs-attribute">13</span>             <span class="hljs-number">3</span>. degree AV block                    <span class="hljs-number">0</span>

   <span class="hljs-attribute">14</span>             Left ventricule hypertrophy                 <span class="hljs-number">4</span>

   <span class="hljs-attribute">15</span>             Atrial Fibrillation or Flutter                <span class="hljs-number">5</span>

   <span class="hljs-attribute">16</span>             Others                           <span class="hljs-number">22</span>

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