Uncertainty quantification in DenseNet model using myocardial infarction ECG signals
Article
Article Title | Uncertainty quantification in DenseNet model using myocardial infarction ECG signals |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Jahmunah, V., Ng, E.Y.K., Tan, Ru-San, Oh, Shu Lih and Acharya, U. Rajendra |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 229 |
Article Number | 107308 |
Number of Pages | 14 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2022.107308 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0169260722006897 |
Abstract | Background and objective Methods Results Conclusion |
Keywords | Deep learning; DenseNet model; Myocardial infarction; Predictive entropy; Uncertainty quantification; Reverse KL divergence |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Nanyang Technological University, Singapore |
National Heart Centre, Singapore | |
Ngee Ann Polytechnic, Singapore | |
School of Mathematics, Physics and Computing | |
Institute for Life Sciences and the Environment | |
Singapore University of Social Sciences (SUSS), Singapore | |
Kumamoto University, Japan | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z1vq5/uncertainty-quantification-in-densenet-model-using-myocardial-infarction-ecg-signals
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