Can I read Uncertainty quantification in DenseNet model using myocardial infarction ECG signals on EtoBox?

Uncertainty quantification in DenseNet model using myocardial infarction ECG signals by V. Jahmunah; E.Y.K. Ng; Ru- San Tan; Shu Lih Oh; U. Rajendra Acharya is a Medicine article available to read on EtoBox.

What is Uncertainty quantification in DenseNet model using myocardial infarction ECG signals about?

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 for reliable MI diagnosis. Methods: A Dirichlet DenseNet model that could analyze out-of-distribution data and detect misclassification of MI and normal ECG signals was developed. The DenseNet model was first trained with the pre-processed MI ECG signals (from the best lead V6) acquired from the Physikalisch-Technische Bundesanstalt (PTB) database, using the reverse Kullback-Leibler (KL) divergence loss. The model was then tested with newly synthesized ECG signals with added em and ma noise samples. Predictive entropy was used as an uncertainty measure to determine the misclassification of normal and MI signals. Model performance was evaluated using four uncertainty metrics: uncertainty sensitivity (UNSE), uncertainty specificity (UNSP), uncertainty accuracy (UNAC), and uncertainty precision (UNPR); the classification threshold was set at 0.3. Results: The UNSE of the DenseNet model was l

Who reads Uncertainty quantification in DenseNet model using myocardial infarction ECG signals?

Author

V. Jahmunah; E.Y.K. Ng; Ru- San Tan; Shu Lih Oh; U. Rajendra Acharya

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)