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: Jahmunah, V, Ng, Eddie Yin Kwee, Tan, Ru-San, Oh, Shu Lih, Acharya, U. Rajendra
其他作者: School of Mechanical and Aerospace Engineering
格式: Article
語言:English
出版: 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