Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records
Article
Article Title | Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records |
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ERA Journal ID | 17908 |
Article Category | Article |
Authors | Baygin, Mehmet, Tuncer, Turker, Dogan, Sengul, Tan, Ru-San and Acharya, U. Rajendra |
Journal Title | Information Sciences |
Journal Citation | 575, pp. 323-337 |
Number of Pages | 15 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0020-0255 |
1872-6291 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2021.06.022 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0020025521006137 |
Abstract | Background and objective Material and method Results Conclusion |
Keywords | Automated arrhythmia detection; Homeomorphically irreducible tree pattern; Maximum absolute pooling; Chi2 feature selection; ECG |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ardahan University, Turkiye |
Firat University Hospital, Turkey | |
National Heart Centre, Singapore | |
Duke-NUS Medical Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z1v71/automated-arrhythmia-detection-with-homeomorphically-irreducible-tree-technique-using-more-than-10-000-individual-subject-ecg-records
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