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A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression

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Abstract

Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic cardiovascular disease diagnosis, e.g., to help prevent stroke or sudden cardiac death. However, the complex individual differences in ECG morphology make it challenging in accurately categorizing arrhythmia heartbeats. To promote robustness of the algorithm for individual differences, we propose a novel ECG arrhythmia classification method with stacked sparse auto-encoders (SSAEs) and a softmax regression (SF) model. The SSAEs is employed to hierarchically extract high-level features from huge amount of ECG data. Features are extracted automatically such that no individual difference in feature selection will bias extraction accuracy. Moreover, the input can be reconstructed completely by the features in each level of the auto-encoder. The SF is then trained to serve as a classifier for discriminating six different types of arrhythmia heartbeats. Computational experiments and comparative analyses are presented to validate the effectiveness of the theoretical models.

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Acknowledgements

This work is partially supported by two grants from the National Natural Science Foundation of China (61203160, 61673158) and the Natural Science Foundation of Hebei Province (F2015201112).

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Authors and Affiliations

  1. Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Hebei, Baoding, China

    Jianli Yang, Yang Bai, Feng Lin, Ming Liu, Zengguang Hou & Xiuling Liu

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Feng Lin

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zengguang Hou

Authors
  1. Jianli Yang
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  2. Yang Bai
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  3. Feng Lin
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  4. Ming Liu
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  5. Zengguang Hou
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  6. Xiuling Liu
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Correspondence to Xiuling Liu.

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Yang, J., Bai, Y., Lin, F. et al. A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression. Int. J. Mach. Learn. & Cyber. 9, 1733–1740 (2018). https://doi.org/10.1007/s13042-017-0677-5

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  • DOI: https://doi.org/10.1007/s13042-017-0677-5

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