Towards Interpretable Arrhythmia Classification With Human-Machine Collaborative Knowledge Representation
IEEE Transactions on Biomedical Engineering(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...
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Key words
Electrocardiography,Knowledge representation,Feature extraction,Man-machine systems,Collaboration,Machine learning,Neural networks
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