Abstract
The First China ECG Intelligent Competition launched ECG challenge to classify 8 kinds of abnormalities from uneven 12-lead ECGs. These abnormalities can be classified into two categories according to morphology and rhythm, four in each group. In this paper, for morphology tasks neural network is applied mainly with input median wave extracted from raw data, while traditional methods are executed and promoted by machine learning to achieve rhythm classification. Non-coexistence relationship is taken into consideration to fit in clinical significance better. The final average F1 score is 0.886 on test set, which certificates these are effective methods for ECG auto detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rahhal, M.M.A., Bazi, Y., Alhichri, H., Alajlan, N., Melgani, F., Yager, R.R.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345(1), 340–354 (2016)
Muthuvel, K., Suresh, L.P., Alexander, T.J., Veni, S.H.K.: Classification of ECG signal using hybrid feature extraction and neural network classifier. In: Kamalakannan, C., Suresh, L.P., Dash, S.S., Panigrahi, B.K. (eds.) Power Electronics and Renewable Energy Systems. LNEE, vol. 326, pp. 1537–1544. Springer, New Delhi (2015). https://doi.org/10.1007/978-81-322-2119-7_150
Sarfraz, M., Khan, A.A., Li, F.F.: Using independent component analysis to obtain feature space for reliable ECG Arrhythmia classification. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 62–67. IEEE (2016)
Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2016)
Acharya, U.R., Fujita, H., Oh, S.L., et al.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415–426, 190–198 (2017)
Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., et al.: Cardiologist-level arrhythmia detection with convolutional neural networks (2017)
Yu, S.N., Chou, K.T.: Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34(4), 2841–2846 (2008)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Liu, W., Zhang, M., Zhang, Y., et al.: Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J. Biomed. Health Inform. 22(5), 1434–1444 (2018)
Li, H., Pu, B., Kang, Y., Lu, C.Y., et al.: Research on massive ECG data in XGBoost. J. Intell. Fuzzy Syst. 36(2), 1161–1169 (2019)
Chen, Y., Wang, X., Jung, Y.H., et al.: Classification of short single-lead electrocardiograms (ECGs for atrial fibrillation detection using piecewise linear spline and XGBoost. Physiol. Meas. 39(10), 104006 (2018)
Shi, H.T., Wang, H.R., Huang, Y.X., et al.: A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. Comput. Methods Program. Biomed. 171, 1–10 (2019)
Acknowledgement
This work is supported by program ykj-2018-00393 of Technology foundation of Beijing University of Technology.
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
Bin, G., Sun, Y., Huang, J., Bin, G. (2019). An Approach to Predict Multiple Cardiac Diseases. 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_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-33327-0_8
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)