Uncertainty quantification in DenseNet model using myocardial infarction ECG signals
Background and objective: Myocardial infarction (MI) is a life-threatening condition diagnosed acutely on the electrocardiogram (ECG). Several errors, such as noise, can impair the prediction of automated ECG diagnosis. Therefore, quantification and communication of model uncertainty are essential f...
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Main Authors: | , , , , |
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格式: | Article |
語言: | English |
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2023
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在線閱讀: | https://hdl.handle.net/10356/172246 |
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機構: | Nanyang Technological University |
語言: | English |