Open Access
| Issue |
BIO Web Conf.
Volume 111, 2024
2024 6th International Conference on Biotechnology and Biomedicine (ICBB 2024)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 6 | |
| Section | Medical Testing and Health Technology Integration | |
| DOI | https://doi.org/10.1051/bioconf/202411103009 | |
| Published online | 31 May 2024 | |
- Wu, Shiyuan. Research on the prediction model of atrial fibrillation based on convolutional and recurrent neural networks[D]. Wuhan University of Science and Technology, 2021. [Google Scholar]
- Wu L.Q., Huang C.X., et al. Chinese expert consensus on transcrystalline balloon catheter ablation of atrial fibrillation[J]. Chinese Journal of Arrhythmia 2020, Vol. 24, No. 2, pp. 96–112. ISTIC, 2021. [Google Scholar]
- ZHANG Peng, WANG Zhinong. Research progress of mobile intelligent terminal in the diagnosis and treatment of atrial fibrillation[J]. China Medical Devices, 2021, 36(3): 5. [Google Scholar]
- NEUBECK L, ORCHARD J, LOWRES N, et al. To Screen or Not to Screen? Examining the Arguments Against Screening for Atrial Fibrillation[J]. Heart Lung & Circulation, 2017: S207741195. [Google Scholar]
- Yang Ping. Research on key technology of arrhythmia recognition problem based on machine learning [D]. Beijing University of Technology, 2020. [Google Scholar]
- FREEDMAN B, CAMM J, CALKINS H, et al. Screening for Atrial Fibrillation A Report of the AFSCREEN International Collaboration[J]. Circulation, 2017, 135(19): 1851. [CrossRef] [PubMed] [Google Scholar]
- CURRY S.J., KRIST A.H., OWENS D.K., et al. Screening for Atrial Fibrillation With Electrocardiography: US Preventive Services Task Force Recommendation Statement [J]. The Journal of the American Medical Association, 2018, 320(5): 478. [CrossRef] [PubMed] [Google Scholar]
- DILAVERIS P.E., KENNEDY H.L. Silent atrial fibrillation: epidemiology, diagnosis, and clinical impact[J]. Clinical cardiology, 2017, 40(6): 413–418. [CrossRef] [PubMed] [Google Scholar]
- G.U. Junnan,ZHANG Muqiu, WANG Jianqiang,LIU Yu, LI Chao. Application of wearable devices in the diagnosis and treatment of atrial fibrillation [J]. Medical Information,2019, 32(15):34–37+41. [Google Scholar]
- RodriguezLeón Ciro, Villalonga Claudia, MunozTorres Manuel, Ruiz Jonatan R, Banos Oresti. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. [J]. JMIR mHealth and uHealth, 2021, 9(6). [Google Scholar]
- Wang YuChiang, Xu Xiaobo, Hajra Adrija, Apple Samuel, Kharawala Amrin, Duarte Gustavo, Liaqat Wasla, Fu Yiwen, Li Weijia, Chen Yiyun, Faillace Robert T.. Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study [J]. Diagnostics, 2022, 12(3). [PubMed] [Google Scholar]
- Lv Tingting, Ding Zijian, Yuan Yifang, et al. Application of deep learning in automatic diagnosis and prediction of cardiovascular diseases by electrocardiogram [J]. Chinese Cardiovascular Journal, 2021, 26(03): 290–293. [Google Scholar]
- Michieli U, Zanuttigh P. Knowledge distillation for incremental learning in semantic segmentation[J]. Computer Vision and Image Understanding, 2021: 103167. [CrossRef] [Google Scholar]
- Frisch D.R. Diagnosing atrial fibrillation by mobile technology: Physician decision or device provision?[J]. Heart (British Cardiac Society), 2020, 106(9): heartjnl-2019-316390. [PubMed] [Google Scholar]
- Wang Mou; Rahardja Sylwan; Fränti Pasi; Rahardja Susanto. Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN [J] Biomedical Signal Processing and Control Volume 79, Issue P1. 2023. [Google Scholar]
- NGUYEN Q.H., NGUYEN B.P., NGUYEN T.B., et al. Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings [J]. Biomedical Signal Processing and Control, 2021,68: 102672. [CrossRef] [Google Scholar]
- Ggnga B, Jdzc A. Automatic classification of atrial fibrillation from short single-lead ECG recordings using a Hybrid Approach of Dual Support Vector Machine. [J] Expert Systems with Applications Volume 198, 2022. [Google Scholar]
- L.I. Quanchi, HUANG Xin, LUO Chengsi, HUANG Huiquan, RAO Ni Ni. Research on the identification method of common arrhythmias by integrating traditional and depth features of single-lead ECG[J]. Chinese Journal of Biomedical Engineering, 2022, 41(01): 31–40. [Google Scholar]
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