Abstract
In this study, we proposed a method based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to classify 12-lead ECG into 9 categories (1 normal, 8 abnormal). The only preprocessing techniques we used are the baseline drift removal based on median filtering and signal segmentation. Then an 18-layer deep 1D CNN consisting of residual blocks and skip architectures that is followed by a bi-directional LSTM layer was developed. During the training session, we suggested a new strategy to compute the Dice loss for multi-label classification. The average F1-score we achieved on the hidden testing dataset of the First China ECG Intelligent Competition (FCEIC) is 85.11%. With the same model, we achieved 82.21% (5-fold cross-validation) on the Chinese physiological signal challenge 2018 (CPSC2018) training dataset.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 61872405 and 61720106004), Key Project of Natural Science Foundation of Guangdong province (2016A030311040), Sichuan Science and Support Program (Grant No. 2015SZ0191) and Chengdu Science and Technology Benefit Plan (2015-HM01-00528-SF).
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Luo, C., Jiang, H., Li, Q., Rao, N. (2019). Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTM. 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_7
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