Deep Learning-Based Approach for Atrial Fibrillation Detection

Lazhar Khriji*, Marwa Fradi, Mohsen Machhout, Abdulnasir Hossen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationThe Impact of Digital Technologies on Public Health in Developed and Developing Countries - 18th International Conference, ICOST 2020, Proceedings
EditorsMohamed Jmaiel, Hamdi Aloulou, Mounir Mokhtari, Bessam Abdulrazak, Slim Kallel
PublisherSpringer
Pages100-113
Number of pages14
ISBN (Print)9783030515164
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event18th International Conference on Smart Homes and Health Telematics, ICOST 2020 - Hammamet, Tunisia
Duration: Jun 24 2020Jun 26 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12157 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Smart Homes and Health Telematics, ICOST 2020
Country/TerritoryTunisia
CityHammamet
Period6/24/206/26/20

Keywords

  • AF detection
  • ANN
  • Confusion matrix
  • ECG-classification
  • Histogram error
  • ROC

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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