Abstract
In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches were considered. First we introduced mutliclass UMCE, the ensemble designed to deal with imbalanced datasets. Secondly, we studied the impact of applying oversampling techniques to a training set. smote without prior majority class undersampling was used as one of the methods. Another method we used was smote with noise introduced to synthetic learning examples. The baseline for our study was a single ResNet network with undersampling of the training set. Mutliclass UMCE proved to be superior compared to the baseline model, but failed to beat the results obtained by a single model with smote applied to training set. Introducing perturbations to signals generated by smote did not bring significant improvement. Future work may consider combining multiclass UMCE with smote.
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
Abadi, M.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/
Li, H., et al.: Visualizing the loss landscape of neural nets. CoRR abs/1712.09913 (2017). http://arxiv.org/abs/1712.09913
He, K., et al.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Bowyer, K.W., et al.: SMOTE: synthetic minority over-sampling technique. CoRR abs/1106.1813 (2011). http://arxiv.org/abs/1106.1813
Jun, T.J., et al.: ECG arrhythmia classification using a 2-D convolutional neural network. CoRR abs/1804.06812 (2018). http://arxiv.org/abs/1804.06812
Chollet, F.E.A.: Keras (2015). https://keras.io
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG heartbeat classification: a deep transferable representation. CoRR abs/1805.00794 (2018). http://arxiv.org/abs/1805.00794
Ksieniewicz, P.: Undersampled majority class ensemble for highly imbalanced binary classification. Proc. Mach. Learn. Res. 1, 1–13 (2010)
Pedregosa, F.E.A.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
van der Walt, S., Colbert, S.C., Varoquaux, G.: The numpy array a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011)
Xiong, Z.E.A.: ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiol. Meas. (2018). https://doi.org/10.1088/1361-6579/aad9ed
Xu, S.S., Mak, M.W., Cheung, C.C.: Towards end-to-end ECG classification with rawsignal extraction and deep neural networks. IEEE J. Biomed. Health Inform. (2019)
Acknowledgments
This work is supported by the Polish National Science Center under the Grant no. UMO-2015/19/B/ST6/01597 as well the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology.
We also wanna thank Michał Leś for lending his computing power resources. Thanks to him this results could be collected and presented.
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
Kozal, J., Ksieniewicz, P. (2019). Imbalance Reduction Techniques Applied to ECG Classification Problem. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-33617-2_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33616-5
Online ISBN: 978-3-030-33617-2
eBook Packages: Computer ScienceComputer Science (R0)