Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

Frontiers in Physiology
Runnan HeHenggui Zhang

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the propose...Continue Reading

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Citations

Jun 9, 2020·Journal of Healthcare Engineering·Hongpo ZhangZongmin Wang
Nov 27, 2020·BMC Medical Informatics and Decision Making·Yusong HuPeizhe Li
Sep 26, 2020·Artificial Intelligence in Medicine·Muhammad Yazid, Mahrus Abdur Rahman
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May 27, 2021·BMC Medical Informatics and Decision Making·Mehmet Akif OzdemirOnan Guren
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Jun 19, 2021·Physical and Engineering Sciences in Medicine·Hocine HamilDjaffar Ould Abdeslam
Sep 14, 2021·Frontiers in Physiology·Ricardo Salinas-MartínezFrida Sandberg

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Methods Mentioned

BETA
Feature extraction

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