A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks-Reference-Cited by-同舟云学术

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A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks

Published:2021-01-08 Issue:6 Volume:25 Page:4571-4595
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Zeng Wei^ORCID,Yuan Jian,Yuan Chengzhi,Wang Qinghui,Liu Fenglin,Wang Ying

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Fujian Provincial Training Foundation For “Bai-Qian-Wan Talents Engineering”

Training Program of Innovation and Entrepreneurship for Undergraduates

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-020-05465-8.pdf

Reference86 articles.

1. Abawajy JH, Kelarev AV, Chowdhury M (2013) Multistage approach for clustering and classification of ECG data. Comput Methods Progr Biomed 112(3):720–730

2. Abdalla FY, Wu L, Ullah H, Ren G, Noor A, Zhao Y (2019) ECG arrhythmia classification using artificial intelligence and nonlinear and nonstationary decomposition. Signal Image Video Process 13(7):1283–1291

3. Acharya UR, Fujita H, Adam M, Lih OS, Hong TJ, Sudarshan VK, Koh JE (2016) Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC), pp 000533–000538

4. Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl Based Syst 132:62–71

5. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396

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