Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals
Article
Seoni, Silvia, Molinari, Filippo, Acharya, U. Rajendra, Oh, Oh Shu, Barua, Prabal Datta, García, Salvador and Salvi, Massimo. 2024. "Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals." Information Sciences. 665. https://doi.org/10.1016/j.ins.2024.120383
Article Title | Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals |
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ERA Journal ID | 17908 |
Article Category | Article |
Authors | Seoni, Silvia, Molinari, Filippo, Acharya, U. Rajendra, Oh, Oh Shu, Barua, Prabal Datta, García, Salvador and Salvi, Massimo |
Journal Title | Information Sciences |
Journal Citation | 665 |
Article Number | 120383 |
Number of Pages | 15 |
Year | 2024 |
Publisher | Elsevier |
ISSN | 0020-0255 |
1872-6291 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2024.120383 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0020025524002962 |
Abstract | This study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using artificial intelligence (AI) models. Despite the progress made in mitigating opacity with explainable AI (XAI) and uncertainty quantification (UQ), understanding the real-world predictive reliability of AI methods remains a challenge. In this study, we propose a novel indicator called the Spatial Uncertainty Estimator (SUE) to assess the prediction reliability of classification networks in practical Electrocardiography (ECG) scenarios. SUE quantifies the spatial overlap of critical Grad-CAM (Gradient-weighted Class Activation Mapping) features, offering a confidence score for predictions. |
Keywords | CAD; Explainable AI ; ECG; Deep learning ; Uncertainty quantification ; Signal processing |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Polytechnic University of Turin, Italy |
School of Mathematics, Physics and Computing | |
Centre for Health Research | |
Cogninet Australia, Australia | |
School of Business | |
University of Technology Sydney | |
University of Granada, Spain |
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