Corpus ID: 231573465

Identification of 27 cardiac abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function

@inproceedings{Zhu2020IdentificationO2,
  title={Identification of 27 cardiac abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function},
  author={Zhaowei Zhu and Xiang Lan and Tingting Zhao and Yangming Guo and Pipin Kojodjojo and Zhuoyang Xu and Zhuo Liu and Siqi Liu and Han Wang and Xingzhi Sun and Mengling Feng},
  year={2020}
}
Objective: Cardiovascular disease is a major threat to health and one of the primary causes of death globally, where the cardiac abnormality is the most common type of cardiovascular disease. The early and accurate diagnosis of cardiac abnormalities will allow early treatment and intervention to prevent further progression of the disease. In accordance with the PhysioNet/Computing in Cardiology Challenge 2020, our objective is to develop an algorithm that automatically identifies 27 types of… Expand

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References

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