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Abstract

Electrocardiogram (ECG) is strong evidence in the diagnosis of a wide range of heart-related diseases, and it is becoming increasingly important in the medical field recently. However, inferencing diseases with ECG signals is both time-consuming and error-prone even for licensed physicians, which arises the urgency of developing a fast and accurate automatic diagnosis algorithm. In this paper, we explore both deep learning models and well-designed feature engineering from ECG waveform. By combining the two methods, we propose an automatic diagnosis framework that can extract meaningful features both with and without human interventions. Experimental results on the ECG competition demonstrate that our framework can reach accurate results on heart-related diseases diagnosis.

Keywords

ECG Deep learning Feature engineering Automatic diagnosis framework 

Notes

Acknowledgements

The work was supported by the National Natural science Foundation of China (NSFC) Projects (Nos. 61673241, 61721003, 61872218), Beijing National Research Center for Information Science and Technology, Tsinghua-Fuzhou Institute research program, and Tsinghua Institute of Data Sciences.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingChina

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