The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings
Open accessAbstract
Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly population, associated with a high mortality and morbidity in stroke, heart failure, coronary artery disease, systemic thromboembolism, etc. The early detection of AF is necessary for averting the possibility of disability or mortality. However, AF detection remains problematic due to its episodic pattern. In this paper, a multiscaled fusion of deep convolutional neural network (MS-CNN) is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The MS-CNN employs the architecture of two-stream convolutional networks with different filter sizes to capture features of different scales. The experimental results show that the proposed MS-CNN achieves 96.99% of classification accuracy on ECG recordings cropped/padded to 5 s. Especially, the best classification accuracy, 98.13%, is obtained on ECG recordings of 20 s. Compared with artificial neural network, shallow single-stream CNN, and VisualGeometry group network, the MS-CNN can achieve the better classification performance. Meanwhile, visualization of the learned features from the MS-CNN demonstrates its superiority in extracting linear separable ECG features without hand-craft feature engineering. The excellent AF screening performance of the MS-CNN can satisfy the most elders for daily monitoring with wearable devices.
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Developed with the special contribution of the European Heart Rhythm Association (EHRA), Endorsed by the European Association for Cardio-Thoracic Surgery (EACTS), Authors/Task Force Members, Camm, A. J., Kirchhof, P., Lip, G. Y. H., Schotten, U., Savelieva, I., Ernst, S., Van Gelder, I. C., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq278 · Cited by 5,864
Cited by
Yao, Q., Wang, R., Fan, X., Liu, J., & Li, Y. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.024 · Cited by 469
Self-citationLSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Saadatnejad, S., Oveisi, M., & Hashemi, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2911367 · Cited by 406
Xie, Y., Lu, L., Gao, F., He, S.-J., Zhao, H.-J., Fang, Y., Yang, J.-M., An, Y., Ye, Z.-W., & Dong, Z. (2021) · Current Medical Science · doi:10.1007/s11596-021-2485-0 · Cited by 234
Lee, S., Chu, Y., Ryu, J., Park, Y. J., Yang, S., & Koh, S. B. (2022) · Yonsei Medical Journal · doi:10.3349/ymj.2022.63.s93 · Cited by 88
Metadata
- Authors
- Fan, X., Yao, Q., Cai, Y., Miao, F., Sun, F., & Li, Y.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2018-11
- Volume
- 22
- Issue
- 6
- Pages
- 1744-1753
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2887119478
- Cited by
- 323
- References
- 29
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Fan, X., Yao, Q., Cai, Y., Miao, F., Sun, F., & Li, Y. (2018). Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2018.2858789
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