{
  "fetched_at": "2026-10-19T08:38:56Z",
  "source_url": "https://www.kaggle.com/competitions/different-anomalies-in-ecgs/data",
  "pages": [
    {
      "name": "rules",
      "content": "Rules of the competition : \n\n - Use RNN to predict the labels\n - The student with the highest score on the public AND private leaderboard will win a free lunch at All You Can Eat Sushi Antony. ",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set, the last column represents the labels </li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<h2>Introduction</h2>\n\n<p>Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. </p>\n<p> The aim of this competition is to develop a method based on deep recurrent neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. </p>",
      "isPublished": false,
      "hasIsPublished": false,
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    {
      "name": "Evaluation",
      "content": "Evaluation is based on the mean F1-score over the different labels. ",
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      "hasIsPublished": false,
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}