Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities-Reference-Cited by-同舟云学术

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Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities

Published:2020-03-23 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Alghamdi Ahmed,Hammad Mohamed,Ugail Hassan,Abdel-Raheem Asmaa,Muhammad Khan,Khalifa Hany S.,Abd El-Latif Ahmed A.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-020-08769-x.pdf

Reference63 articles.

1. Acharya UR, Fujita H, Sudarshan VK, Shu LO, Adam M, Koh JEW et al (2016) Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 99:146–156

2. Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M (2017) Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf Sci 415-416:190–198

3. Al-Kindi SG, Ali F, Farghaly A, Nathani M, Tafreshi R (2011) Towards real-time detection of myocardial infarction by digital analysis of electrocardiograms. IEEE, 1st Middle East Conference on Biomedical Engineering, p.454–457

4. Amrani M, Hammad M, Jiang F, Wang K, Amrani A (2018) Very deep feature extraction and fusion for arrhythmias detection. Neural Comput Applic 30(7):2047–2057

5. Arif M, Malagore IA, Afsar FA (2012) Detection and localization of myocardial infarction using k-nearest neighbor classifier. J Med Syst 36(1):279–289

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