<li>MIT-BIH Arrhythmia Database [<strong>mitdb</strong>]</li>

<li>MIT-BIH Arrhythmia Database [<strong>mitdb</strong>]</li>

<li>MIT-BIH Supraventricular Arrhythmia Database [<strong>svdb</strong>]</li>

<li>MIT-BIH Supraventricular Arrhythmia Database [<strong>svdb</strong>]</li>

<li>St Petersburg INCART 12-lead Arrhythmia Database [<strong>incartdb</strong>]</li>

<li>St Petersburg INCART 12-lead Arrhythmia Database [<strong>incartdb</strong>]</li>

</ol>

</ol>

<h3>NOTE</h3>

<h3>NOTE</h3>

  <ol>

  <ol>

  <li>All signals have been resampled to 128Hz and gain has been removed.</li>

  <li>All signals have been resampled to 128Hz and gain has been removed.</li>

  <li>Baseline wander has been removed using Median Filtering.</li>

  <li>Baseline wander has been removed using Median Filtering.</li>

  <li>Denoising was NOT used.</li>

  <li>Denoising was NOT used.</li>

  <li>Signal data and annotation labels have been saved in numpy (.npy) format.</li>

  <li>Signal data and annotation labels have been saved in numpy (.npy) format.</li>

  <li>All Signals are nearly 30 mins long.</li>

  <li>All Signals are nearly 30 mins long.</li>

  </ol>

  </ol>

<h3>CONTENT</h3>

<h3>CONTENT</h3>

<p>Data has been organised as follows:</p>

<p>Data has been organised as follows:</p>

<p>parent directory <em>db_npy</em> contains 3 sub-directories each of which represent one database</p>

<p>parent directory <em>db_npy</em> contains 3 sub-directories each of which represent one database</p>

  <p>mitdb_npy has 48 records</p>

  <p>mitdb_npy has 48 records</p>

  <p>svdb_npy has 78 records</p>

  <p>svdb_npy has 78 records</p>

  <p>incartdb_npy has 75 records</p>

  <p>incartdb_npy has 75 records</p>

<p>Each of these database directory contains a '<strong><em>RECORDS</em></strong>' file that lists the ecg records available in that database.</p>

<p>Each of these database directory contains a '<strong><em>RECORDS</em></strong>' file that lists the ecg records available in that database.</p>

<p>Each record has 3 files associated with it:</p>

<p>Each record has 3 files associated with it:</p>

  <ol>

  <ol>

  <li><p><em>rec</em>_BEAT.npy: contains 'beat' annotations (R-peaks and its label) for the record.<br>

  <li><p><em>rec</em>_BEAT.npy: contains 'beat' annotations (R-peaks and its label) for the record.<br>

  <em>each record may have variable number of beats based on heart rate</em><br>

  <em>each record may have variable number of beats based on heart rate</em><br>

  <em>usually we shall be interested in beat annotation labels only. Each beat label represents one R-peak and hence one beat</em></p></li>

  <em>usually we shall be interested in beat annotation labels only. Each beat label represents one R-peak and hence one beat</em></p></li>

  <li><p><em>rec</em>_NBEAT.npy: contains 'non-beat' annotations (Other than R-peaks) for the record</p></li>

  <li><p><em>rec</em>_NBEAT.npy: contains 'non-beat' annotations (Other than R-peaks) for the record</p></li>

  <li><p><em>rec</em>_SIG_II.npy: contains the Lead 2 signal data of the record as a single numpy array</p></li>

  <li><p><em>rec</em>_SIG_II.npy: contains the Lead 2 signal data of the record as a single numpy array</p></li>

  </ol>

  </ol>

<p>(* see the introduction kernel on how to access data and use annotation labels *)</p>

<p>(* see the introduction kernel on how to access data and use annotation labels *)</p>

<h3>Understanding Annotations:</h3>

<h3>Understanding Annotations:</h3>

<p>There are two types of annotations: Beat and Non-Beat annotations.<br>

<p>There are two types of annotations: Beat and Non-Beat annotations.<br>

Beat annotations are associated with each heart-beat. If you are working with heart-beat classifications then only Beat annotations shall be useful and Non-Beat annotations can be ignored.</p>

Beat annotations are associated with each heart-beat. If you are working with heart-beat classifications then only Beat annotations shall be useful and Non-Beat annotations can be ignored.</p>

<p>Standard PhysioNet Annotations are described in <strong><em>db_npy/annotations.txt</em></strong> file. These are common across all databases.<br>

<p>Standard PhysioNet Annotations are described in <strong><em>db_npy/annotations.txt</em></strong> file. These are common across all databases.<br>

<p><img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5417013%2F43781477b37cdca4adb187e4b39c3648%2Fstdlab.png?generation=1593929940178708&amp;alt=media"></p>

<p><img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5417013%2F43781477b37cdca4adb187e4b39c3648%2Fstdlab.png?generation=1593929940178708&amp;alt=media"></p>

<p>There are 19 Beat annotations and 22 Non-Beat annotations. However, not all annotations may occur in data files. For example, the Label 'r' does not occur even once in any three of the database but yet its the part of standard PhysioNet labels. (might be in use in some other database). It's advised to do a full annotation count before working with data.</p>

<p>There are 19 Beat annotations and 22 Non-Beat annotations. However, not all annotations may occur in data files. For example, the Label 'r' does not occur even once in any three of the database but yet its the part of standard PhysioNet labels. (might be in use in some other database). It's advised to do a full annotation count before working with data.</p>

<p>According to AAMI recommendation, each beat is classified into one of the 5 types [ N, V, S, F, Q ]. However, you are free to choose any classification strategy.</p>

<p>According to AAMI recommendation, each beat is classified into one of the 5 types [ N, V, S, F, Q ]. However, you are free to choose any classification strategy.</p>

<p><img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5417013%2F58397c44c2e877ddb61418270eb2f3fd%2FRelation-between-MIT-BIH-heartbeats-and-AAMI-standards.png?generation=1593940064290748&amp;alt=media"></p>

<p><img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5417013%2F58397c44c2e877ddb61418270eb2f3fd%2FRelation-between-MIT-BIH-heartbeats-and-AAMI-standards.png?generation=1593940064290748&amp;alt=media"></p>

<h3>IMPORTANT</h3>

<h3>IMPORTANT</h3>

  <ol>

  <ol>

  <li><p>mitdb's record '102' and '104' DO NOT have lead 2 signal available hence files '102_SIG_II.npy' and '104_SIG_II.npy' are not present. However, they have BEAT and NBEAT files present. Its advised not to use those two records</p></li>

  <li><p>mitdb's record '102' and '104' DO NOT have lead 2 signal available hence files '102_SIG_II.npy' and '104_SIG_II.npy' are not present. However, they have BEAT and NBEAT files present. Its advised not to use those two records</p></li>

  <li><p>mitdb's record '102', '104', '107' and '217' are paced records</p></li>

  <li><p>mitdb's record '102', '104', '107' and '217' are paced records</p></li>

  <li><p>mitdb's record '207' is the only record with 'Flutter' waves that are not marked by beat-annotations (no R-peaks marked). However, they are marked by non-beat annotations.</p></li>

  <li><p>mitdb's record '207' is the only record with 'Flutter' waves that are not marked by beat-annotations (no R-peaks marked). However, they are marked by non-beat annotations.</p></li>

  </ol>

  </ol>

<h3>Acknowledgements</h3>

<h3>Acknowledgements</h3>

<p>**PhysioNet **[<a rel="noreferrer nofollow" aria-label="https://physionet.org/ (opens in a new tab)" target="_blank" href="https://physionet.org/">https://physionet.org/</a>]<br>

<p>**PhysioNet **[<a rel="noreferrer nofollow" aria-label="https://physionet.org/ (opens in a new tab)" target="_blank" href="https://physionet.org/">https://physionet.org/</a>]<br>

MLA    Goldberger, A., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).<br>

MLA    Goldberger, A., et al. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).<br>

APA        Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … &amp; 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.<br>

APA        Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., … &amp; 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.<br>

Chicago        Goldberger, A., L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).<br>

Chicago        Goldberger, A., L. Amaral, L. Glass, J. Hausdorff, P. C. Ivanov, R. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220." (2000).<br>