Improving ECG Classification Interpretability Using Saliency Maps

Jones, Y., Deligianni, F. and Dalton, J. (2020) Improving ECG Classification Interpretability Using Saliency Maps. In: 20th IEEE International Conference on BioInformatics and BioEngineering, 26-28 Oct 2020, pp. 675-682. ISBN 9781728195742 (doi: 10.1109/BIBE50027.2020.00114)

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Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to be analyzed by a cardiologist, taking time which could be spent on improving patient care and outcomes.Because of this, automatic ECG classification systems using machine learning have been proposed, which can learn complex interactions between ECG features and use this to detect abnormalities. However, algorithms built for this purpose often fail to generalize well to unseen data, reporting initially impressive results which drop dramatically when applied to new environments. Additionally, machine learning algorithms suffer a ‘black-box’ issue, in which it is difficult to determine how a decision has been made. This is vital for applications in healthcare, as clinicians need to be able to verify the process of evaluation in order to trust the algorithm.This paper proposes a method for visualizing model decisions across each class in the MIT-BIH arrhythmia dataset, using adapted saliency maps averaged across complete classes to determine what patterns are being learned. We do this by building two algorithms based on state-of-the-art models. This paper highlights how these maps can be used to find problems in the model which could be affecting generalizability and model performance. Comparing saliency maps across complete classes gives an overall impression of confounding variables or other biases in the model, unlike what would be highlighted when comparing saliency maps on an ECG-by-ECG basis.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani and Dalton, Dr Jeff and Jones, Yola
Authors: Jones, Y., Deligianni, F., and Dalton, J.
College/School:College of Science and Engineering > School of Computing Science
Published Online:16 December 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 20th IEEE International Conference on BioInformatics and BioEngineering: 675-682
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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