Abstract
An electrocardiogram (ECG) record potentially contains multiple abnormalities concurrently, therefore multi-label classification of ECG is significant in clinical scenarios. In this paper, we propose an ensemble neural network to address the multi-label classification of 12-lead ECG. The proposed network contains two modules, which treat the multi-label task from two different perspectives. The first module deals with the task in a sequence-generation manner by a novel encoder-decoder structure. The second module treats the multi-label problem as multiple binary classification tasks, by employing two convolutional neural networks of different structure. Finally, the predictions of two modules are integrated as the final result. Our method is trained and evaluated on the dataset provided by the First China ECG Intelligent Competition, and yields a Macro-\(F_1\) of 0.872 on the test set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Martinez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P.: A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)
Gao, P., Zhao, J., Wang, G., Guo, H.: Real time ECG characteristic point detection with randomly selected signal pair difference (RSSPD) feature and random forest classifier. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, USA, pp. 732–735. IEEE (2016)
Xia, Z., et al.: Real-time ECG delineation with randomly selected wavelet transform feature and random walk estimation. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Hawaii, USA, pp. 2691–2694. IEEE (2018)
Vijayavanan, M., Rathikarani, V., Dhanalakshmi, P.: Automatic classification of ECG signal for heart disease diagnosis using morphological features. Int. J. Comput. Sci. Eng. Technol. (IJCSET) 5(4), 449–555 (2014)
Korurek, M., Dogan, B.: ECG beat classification using particle swarm optimization and radial basis function neural network. Expert Syst. Appl. 37(12), 7563–7569 (2010)
Park, K.S., et al.: Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function. In: Computers in Cardiology, Bologna, Italy, pp. 229–232. IEEE (2008)
Hannun, A.Y., et al.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65–69 (2019)
Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infraction using ECG signals. Inf. Sci. 415–416, 190–198 (2017)
The First China ECG Intelligent Competition. http://mdi.ids.tsinghua.edu.cn. Accessed 28 June 2019
Yang, P.C., Sun, X., Li, W., Ma, S.M., Wu, W., Wang, H.F.: SGM: sequence generation model for multi-label classification. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3915–3926. Association for Computational Linguistics, New Mexico, USA (2018)
Huang, G., Liu, Z., Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hl, USA, pp. 2261–2269. IEEE (2017)
Vaswani, A., et al.: Attention is all you need. In: 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA, pp. 2261–2269 (2017)
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 1412–1421. Association for Computational Linguistics (2015)
Wiseman, S., Rush, A.M.: Sequence-to-sequence learning as beam-search optimization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, USA, pp. 1296–1306. Association for Computational Linguistics (2016)
He, K.M., Zhang, X.Y., Ren, S.Q., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770–779. IEEE (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 2015 International Conference on Learning Representations (ICLR), San Diego, USA, pp. 770–779 (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jia, D. et al. (2019). An Ensemble Neural Network for Multi-label Classification of Electrocardiogram. 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_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-33327-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33326-3
Online ISBN: 978-3-030-33327-0
eBook Packages: Computer ScienceComputer Science (R0)
