Uncertainty quantification in DenseNet model using myocardial infarction ECG signals

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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Bibliographic Details
Main Authors: Jahmunah, V, Ng, Eddie Yin Kwee, Tan, Ru-San, Oh, Shu Lih, Acharya, U. Rajendra
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Mechanical engineering
Uncertainty Quantification
DenseNet Model
Online Access:https://hdl.handle.net/10356/172246

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