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Cai, W., Chen, Y., Guo, J., et al. (2020) Accurate Detection of Atrial Fibrillation from 12-Lead ECG Using Deep Neural Network. Computers in Biology and Medicine, 116, Article 103378.
https://doi.org/10.1016/j.compbiomed.2019.103378

has been cited by the following article:

• TITLE: Research Progress of Atrial Fibrillation Detection Technology

  AUTHORS: Yingying Yang, Xue Dong, Yuxi Liang

  KEYWORDS: Atrial Fibrillation, ECG, Detection Technology

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.12 No.4, April 3, 2024

  ABSTRACT: Atrial fibrillation (AF) is the most common chronic arrhythmia in clinical practice, which can cause high disability and mortality with the progress of the disease. Many studies at home and abroad have shown that the incidence of atrial fibrillation gradually increases with age. Clinically, the onset of most AF patients is insidious, which is difficult to capture by routine electrocardiogram, and there is some difficulty in the diagnosis. In order to make the early diagnosis of atrial fibrillation more efficient and accurate, this paper reviews the current status and research progress of detection technology for atrial fibrillation at home and abroad, in order to provide a scientific basis for the early diagnosis of atrial fibrillation.