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Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Author

Listed:
  • Fatma Murat

    (Department of Electrical and Electronics Engineering, Firat University, Elazig 23000, Turkey)

  • Ferhat Sadak

    (Department of Mechanical Engineering, Bartin University, Bartin 74100, Turkey)

  • Ozal Yildirim

    (Department of Software Engineering, Firat University, Elazig 23000, Turkey)

  • Muhammed Talo

    (Department of Software Engineering, Firat University, Elazig 23000, Turkey)

  • Ender Murat

    (Department of Cardiology, Gülhane Training and Research Hospital, Ankara 06000, Turkey)

  • Murat Karabatak

    (Department of Software Engineering, Firat University, Elazig 23000, Turkey)

  • Yakup Demir

    (Department of Electrical and Electronics Engineering, Firat University, Elazig 23000, Turkey)

  • Ru-San Tan

    (Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore
    Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore)

  • U. Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 138607, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore)

Abstract

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.

Suggested Citation

  • Fatma Murat & Ferhat Sadak & Ozal Yildirim & Muhammed Talo & Ender Murat & Murat Karabatak & Yakup Demir & Ru-San Tan & U. Rajendra Acharya, 2021. "Review of Deep Learning-Based Atrial Fibrillation Detection Studies," IJERPH, MDPI, vol. 18(21), pages 1-17, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11302-:d:666435
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    References listed on IDEAS

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    1. Oliver Faust & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "A Review of Atrial Fibrillation Detection Methods as a Service," IJERPH, MDPI, vol. 17(9), pages 1-34, April.
    2. Narin, Ali & Isler, Yalcin & Ozer, Mahmut & Perc, Matjaž, 2018. "Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 56-65.
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    Cited by:

    1. Yujia Wang & Zhe Chen & Sen Tian & Shuxun Zhou & Xinbo Wang & Ling Xue & Jianhui Wu, 2022. "Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers," IJERPH, MDPI, vol. 20(1), pages 1-17, December.

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