Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Fatma Murat, Ferhat Sadak, Ozal Yildirim, Muhammed Talo, Ender Murat, Murat Karabatak, Yakup Demir, Ru-San Tan and U. Rajendra Acharya
Additional contact information
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
U. Rajendra Acharya: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 138607, Singapore

IJERPH, 2021, vol. 18, issue 21, 1-17

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.

Keywords: atrial fibrillation; ECG; deep learning; deep neural networks; arrhythmia detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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