Abstract
The First China ECG Intelligent Competition is held by Tsinghua University. It is aimed to intelligently classify electrocardiogram (ECG) signals into two categories in preliminary and nine categories in rematch. The detailed ECG categories are listed in subsequent section. Our team proposes a deep residual network for diagnosing cardiovascular diseases automatically based on ECG, making full use of the network’s hierarchical feature learning and feature representation ability. Considering that the amount of this competition data is small, especially in the stage of preliminary where there are only 600 training samples, while the deep learning-based method is data-hungry. Transfer learning idea is introduced into the training process of proposed deep neural networks. The proposed network is firstly trained on the Physionet/CinC Challenge 2017 dataset that is an open-public ECG data with single lead. Then it is continuously fine-tuned on the competition dataset with 12 leads. The performance of the proposed network is improved a lot. The proposed method achieves \( F_{1} \) score of 0.89 and 0.86 in the hidden test set of preliminary and rematch, respectively. The research code will be released later.
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Acknowledgements
This research is supported by grant with number 51775415, 2017YFC1308500, and 2018ZDCXL-GY-06-01. Thank Tsinghua University and other member of the organizing committee for providing ECG dataset and platform.
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Chen, L., Xu, G., Zhang, S., Kuang, J., Hao, L. (2019). Transfer Learning for Electrocardiogram Classification Under Small Dataset. In: Liao, H., et al. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH CVII-STENT 2019 2019. Lecture Notes in Computer Science(), vol 11794. Springer, Cham. https://doi.org/10.1007/978-3-030-33327-0_6
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