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Review
. 2021 Oct 28;18(21):11302.
doi: 10.3390/ijerph182111302.

Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Fatma Murat  1 , Ferhat Sadak  2 , Ozal Yildirim  3 , Muhammed Talo  3 , Ender Murat  4 , Murat Karabatak  3 , Yakup Demir  1 , Ru-San Tan  5   6 , U Rajendra Acharya  7   8   9
Affiliations
Review

Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Fatma Murat et al. Int J Environ Res Public Health. .

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: ECG; arrhythmia detection; atrial fibrillation; deep learning; deep neural networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of publications on atrial fibrillation detection using deep learning by year of publication (a) and type of model deployed (b). CNN, convolutional neural network; DNN, deep neural network; LSTM, long short-term memory; RNN, recurrent neural network.
Figure 2
Figure 2
Block diagram representation of the general approach for deep learning-based atrial fibrillation detection.
Figure 3
Figure 3
Block representation of ECG signal formats that can be input to deep learning models for atrial fibrillation detection.
Figure 4
Figure 4
A block representation of cloud-based atrial fibrillation detection system using ECG records.
See this image and copyright information in PMC

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