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Short Term ECG Classification with Residual-Concatenate Network and Metric Learning

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Abstract

ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China-Xinjiang Joint Fund under Grant U1903127 and in part by the Key Research and Development Project of Shandong Province under Grant 2018GGX101032, 2019GGX101056, 2019JZZY010706.

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Authors and Affiliations

  1. School of Software, Shandong University, Jinan, 250101, China

    Xinjing Song, Gongping Yang, Kuikui Wang & Yilong Yin

  2. School of Computer, Heze University, Heze, 274015, China

    Gongping Yang & Yuwen Huang

  3. Key Laboratory of TCM Data Cloud Service in Universities of Shandong, Shandong Management University, Jinan, 250357, China

    Feng Yuan

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  1. Xinjing Song
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  2. Gongping Yang
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  3. Kuikui Wang
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  4. Yuwen Huang
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  5. Feng Yuan
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  6. Yilong Yin
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Correspondence to Gongping Yang.

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Song, X., Yang, G., Wang, K. et al. Short Term ECG Classification with Residual-Concatenate Network and Metric Learning. Multimed Tools Appl 79, 22325–22336 (2020). https://doi.org/10.1007/s11042-020-09035-w

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