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Towards Interpretable Arrhythmia Classification With Human-Machine Collaborative Knowledge Representation.
- Source :
- IEEE Transactions on Biomedical Engineering; Jul2021, Vol. 68 Issue 7, p2098-2109, 12p
- Publication Year :
- 2021
-
Abstract
- Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular diseases. However, deep learning models that are commonly used and trained in end-to-end fashion are not able to provide good interpretability. In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative knowledge representation. Our approach first employs an AutoEncoder to encode electrocardiogram signals into two parts: hand-encoding knowledge and machine-encoding knowledge. A classifier then takes as input the encoded knowledge to classify arrhythmia heartbeats with or without human in the loop (HIL). Experiments and evaluation on the MIT-BIH Arrhythmia Database demonstrate that our new approach not only can effectively classify arrhythmia while offering interpretability, but also can improve the classification accuracy by adjusting the hand-encoding knowledge with our HIL mechanism. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189294
- Volume :
- 68
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Biomedical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 151250141
- Full Text :
- https://doi.org/10.1109/TBME.2020.3024970