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A new intelligent ECG recognition approach based on CNN and improved ALO-SVM

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Abstract

Cardiovascular disease is one of the most common diseases, which seriously threatens people’s life and health. Therefore, cardiovascular disease prevention becomes one of the most attractive research topics in health care system design. Intelligent recognition of electrocardiogram (ECG) signals represents an effective method for rapid diagnosis and the evaluation of cardiovascular diseases in medicine. Realization and efficiency of the classification of ECG signals in real time play major roles in the detection of cardiovascular diseases. This paper is concerned with the proposition of an intelligent ECG signal recognition method based on a convolutional neural network (CNN) and support vector machines (SVM) with an improved antlion algorithm (ALO). First, the ECG signal is denoised and pre-processed by lifting the wavelet. Subsequently, CNN is used to extract the signal characteristics of the denoising signal, and the extracted signal characteristics are used as the input characteristics of the SVM. Finally, an improved ALO algorithm is used to optimize the relevant input functions of the SVM to achieve a better signal classification. In our algorithm, the performance is enhanced by improving the threshold estimation method of the lifting wavelet, to improve the filtering effect. The proposed CNN architecture is tested with multi-lead ECG signals from the MIT-BIH ECG signal data set. The results display that the method has obtained an average accuracy, sensitivity, and specificity values of \(99.97\%\), \(99.97\%\), and \(99.99\%\), respectively. Compared with the existing results, the proposed approach has a better recognition performance.

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Data Availability

The datasets used or analyzed during this study are available in the MIT-BIH database(https://www.physionet.org/content/mitdb).

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Acknowledgements

This work was supported in part by the National Key R &D Funding under Grant 2018YFE0206900, and the National Natural Science Foundation of China under Grant 61873237.

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Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China

    Zhitao He, Yongyi Chen & Dan Zhang

  2. Wuxi Bozhixin Technology Co., Ltd, Wuxi, 214000, Jiangsu, China

    Wutao Yin

  3. Department of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy

    Hamid Reza Karimi

Authors
  1. Zhitao He
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  2. Yongyi Chen
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  3. Dan Zhang
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  4. Wutao Yin
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  5. Hamid Reza Karimi
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Contributions

ZH conducted research on ECG algorithm and wrote the paper. YC participated in the design and refinement of the study. DZ participated in its design and coordination and helped draft the manuscript. WY and HRK participated in the writing and preparation of the paper. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Dan Zhang.

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He, Z., Chen, Y., Zhang, D. et al. A new intelligent ECG recognition approach based on CNN and improved ALO-SVM. SIViP 17, 965–972 (2023). https://doi.org/10.1007/s11760-022-02300-5

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  • DOI: https://doi.org/10.1007/s11760-022-02300-5

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