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Deep Learning-based Approach for Atrial Fibrillation Detection

EasyChair Preprint no. 2838

6 pagesDate: March 3, 2020


Atrial Fibrillation (AF) is a health-threatening condition, which is a violation of the heart rhythm that can lead to heart-related complications. Remarkable interest has been given to ECG signals analysis for AF detection in an early stage. In this context, we propose an artificial neural network ANN application to classify ECG signals into three classes, the first presents Normal Sinus Rhythm NSR, the second depicts abnormal signal with Atrial Fibrillation (AF) and the third shows noisy ECG signals. Accordingly, we achieve 93.1% accuracy classification results, 92% of sensitivity and 90% of specificity. Furthermore, we yield a value of zero error and a low value of cross entropy, which prove the robustness of the proposed ANN model architecture. Thus, we outperforms the state of the art by achieving high accuracy classification without pre-processing step and without high level of feature extraction, and then we enable clinicians to determine automatically the class of each patient ECG signal.

Keyphrases: AF detection, ANN, Confusion Matrix, ECG classification, Histogram error, ROC

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Lazhar Khriji and Marwa Fradi and Mohsen Machhout and Abdulnasir Hossen},
  title = {Deep Learning-based Approach for Atrial Fibrillation Detection},
  howpublished = {EasyChair Preprint no. 2838},

  year = {EasyChair, 2020}}
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