Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks
We train an enhanced deep convolutional neural network in order to identify eight cardiac abnormalities from the standard 12-lead electrocardiograms (ECGs) using the dataset of 14000 ECGs. Instead of straightforwardly applying an end-to-end deep learning approach, we find that deep convolutional neural networks enhanced with sophisticated hand crafted features show advantages in reducing generalization errors. Additionally, data preprocessing and augmentation are essential since the distribution of eight cardiac abnormalities are highly biased in the given dataset. Our approach achieves promising generalization performance in the First China ECG Intelligent Competition; an empirical evaluation is also provided to validate the efficacy of our design on the competition ECG dataset.
KeywordsElectrocardiogram Deep convolutional neural network Heart disease diagnosis
Thanks to the committee for their great effort of organizing the First China ECG Intelligent Competition and the anonymous reviewers for their insightful feedback on earlier versions of this paper.
- 1.The first edition of the artificial intelligence competition of cardiovascular disease diagnosis (2019). http://mdi.ids.tsinghua.edu.cn. Accessed 14 July 2019
- 2.Boureau, Y.L., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 111–118 (2010)Google Scholar
- 3.Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Min. Knowl. Discov. 1–47 (2019)Google Scholar
- 6.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
- 9.Hughes, J.W., Joseph, A.D., Gonzalez, J.E.: Using multitask learning to improve 12-lead electrocardiogram classification. arXiv preprint arXiv:1812.00497 (2018)
- 10.Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
- 11.Joshi, S.L., Vatti, R.A., Tornekar, R.V.: A survey on ECG signal denoising techniques. In: 2013 International Conference on Communication Systems and Network Technologies, pp. 60–64. IEEE (2013)Google Scholar
- 12.Khan, M., Aslam, F., Zaidi, T., Khan, S.A.: Wavelet based ECG denoising using signal-noise residue method. In: 2011 5th International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4. IEEE (2011)Google Scholar
- 13.Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
- 15.Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)Google Scholar
- 19.Zhang, L., Aggarwal, C., Qi, G.J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149. ACM (2017)Google Scholar