Uncertainty quantification in DenseNet model using myocardial infarction ECG signals

• 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: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Mechanical engineering; Uncertainty Quantification; DenseNet Model
• Online Access: https://hdl.handle.net/10356/172246
• Institution: Nanyang Technological University

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...