Selected article for: "accuracy specificity sensitivity and achieve accuracy"
Author: Khriji, Lazhar; Fradi, Marwa; Machhout, Mohsen; Hossen, Abdulnasir
Title: Deep Learning-Based Approach for Atrial Fibrillation Detection
  • Cord-id: ohx6cnag
  • Document date: 2020_5_31
    • ID: ohx6cnag
    Snippet: 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 si
    Document: 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, 95.1% of sensitivity, 90.5% of specificity and 98%. 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 outperform 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.

    Search related documents:
    Co phrase search for related documents
    • accuracy achieve and active classification: 1
    • accuracy classification and achieve result: 1
    • accuracy classification and active classification: 1
    • accuracy classification and long interval: 1
    • accuracy rate and actual predicted: 1, 2
    • accuracy result and achieve hard: 1
    • accuracy result and achieve result: 1, 2
    • accuracy result and actual predicted: 1, 2
    • accuracy specificity and active classification: 1, 2
    • accuracy specificity and actual predicted: 1, 2
    • achieve result and actual predicted: 1