ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.
ECG Heartbeat Classification: A Deep Transferable Representation
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abstract
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Interpretable and Steerable Sequence Learning via Prototypes
ProSeNet learns a sparse set of prototypes for case-based explanations in deep sequence models, matches state-of-the-art accuracy on several tasks, and supports manual prototype refinement by non-experts.