Two Will Do: CNN With Asymmetric Loss, Self-Learning Label Correction, and Hand-Crafted Features for Imbalanced Multi-Label ECG Data Classification
Abstract
In this work we present a machine learning approach that is able to classify 30 cardiac abnormalities from an arbitrary number of electrocardiogram (ECG) leads. Features extracted by a deep convolutional neural network are combined with hand-crafted features (demographic, morphological, and heart rate variability metrics) and fed into a multi-layer perceptron. We employ an Asymmetric Loss(ASL) function, which enables the model to focus on hard,but under-represented, samples. To mitigate the issue of ground-truth mislabeling and to provide robustness, we investigate the use of a self-learning label correction method that iteratively estimates correct labels during training. Leaderboard results show our team SMS+1 achieved challenge scores of 0.57 0.58 0.57.56 0.57 for twelve, six, four,three, and two-lead, respectively. Our model maintains the same diagnostic potential on both standard twelve-lead ECGs and reduced-lead ECGs Show more
Publication status
publishedExternal links
Book title
2021 Computing in Cardiology (CinC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
02073 - Rehabilitation Engin. and Science Center / Rehabilitation Engin. and Science Center03654 - Riener, Robert / Riener, Robert
Funding
193291 - Digital Tools For Mental Health: Closing The Loop For Personalized Treatment (SNF)
Notes
Conference lecture held on September 14, 2021.More
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