Key result
An interpretable arrhythmia classification approach using human-machine collaborative knowledge representation effectively classified arrhythmias and improved accuracy with a human-in-the-loop mechanism.
Why the study?
Commonly used end-to-end deep learning models for arrhythmia detection and classification lack good interpretability.
A novel human-machine collaborative knowledge representation approach improves the interpretability and accuracy of deep learning models for ECG arrhythmia classification.
Human-machine collaboration may boost ECG model interpretability; leaves open prospective clinical validation before adoption.
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.
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Wang et al. (2020) studied Arrhythmia. Interpretable arrhythmia classification approach based on human-machine collaborative knowledge representation vs. Without human in the loop was evaluated on Arrhythmia classification accuracy and interpretability. An interpretable arrhythmia classification approach using human-machine collaborative knowledge representation effectively classified arrhythmias and improved accuracy with a human-in-the-loop mechanism.
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