Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection

Author:

Zhang Xiangyu,Li Jianqing,Cai Zhipeng,Zhang Li,Chen Zhenghua,Liu ChengyuORCID

Funder

the National Key Research and Development Program of China

Distinguished Young Scholars of Jiangsu Province

National Natural Science Foundation of China

Primary Research & Development Plan of Jiangsu Province

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Reference42 articles.

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2. Liu C, Zhang X, Zhao L, Liu F, Chen X, Yao Y, Li J (2019) Signal quality assessment and lightweight qrs detection for wearable ecg smartvest system. IEEE Internet Things J 6(2):1363–1374

3. Clifford GD, Liu C, Moody B, Li-wei HL, Silva I, Li Q, Johnson A, Mark RG (2017) AF classification from a short single lead ecg recording: the physionet/computing in cardiology challenge 2017. In: 2017 Computing in Cardiology (CinC), vol 44. IEEE, pp 1–4

4. Hayano J, Kisohara M, Masuda Y, Yuda E (2019) Detection of paroxysmal atrial fibrillation by lorenz plot imaging of ecg rr intervals. In: International forum on medical imaging in Asia 2019, vol 11050. SPIE, pp 297–301

5. Platonov PG, Corino VDA (2018) A clinical perspective on atrial fibrillation. In: Sörnmo L (ed) Atrial fibrillation from an engineering perspective. Series in BioEngineering. Springer, pp 1–24

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