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<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"><p>-- A 3-Classes Dataset:</p> |
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<ul> |
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<li><p>The normal sinus rhythm (NSR) class: MIT-BIH database</p></li> |
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<li><p>The arrhythmia (ARR) class: MIT-BIH database</p></li> |
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<li><p>The congestive heart failure (CHF) class: BIDMC database </p></li> |
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<li><p>Each class has 3930 vectors (extracted from 30 recordings) </p></li> |
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<li><p>A total of 11,790 feature vectors extracted out of the 90 recordings.</p></li> |
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<li><p>Each row in the file represents one sample of length 500.</p></li> |
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<li><p>The last value in each row (element 501) represents the respective calss (0, 1, or 2)</p></li> |
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</ul> |
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<p>Check and cite the paper: <a rel="noreferrer nofollow" aria-label="https://www.mdpi.com/2682016 (opens in a new tab)" target="_blank" href="https://www.mdpi.com/2682016">https://www.mdpi.com/2682016</a> <br> |
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Eleyan, A.; Alboghbaish, E. Electrocardiogram Signals Classification Using Deep-Learning-Based Incorporated Convolutional Neural Network and Long Short-Term Memory Framework. Computers 2024, 13, 55. <a rel="noreferrer nofollow" aria-label="https://doi.org/10.3390/computers13020055 (opens in a new tab)" target="_blank" href="https://doi.org/10.3390/computers13020055">https://doi.org/10.3390/computers13020055</a></p> |
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<p><img alt="" src="https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20702870%2F1a6d7b035cbca6d0ae37aeb47f0c1233%2FExample%20.png?generation=1716019579842814&alt=media"></p></div></div></div> |