iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s11760-022-02300-5
A new intelligent ECG recognition approach based on CNN and improved ALO-SVM | Signal, Image and Video Processing Skip to main content
Springer Nature Link
Log in

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

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

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

References

  1. Ashkezari-Toussi, S., Sabzevari, V.R.: Early arrhythmia prediction based on hurst index and ecg prediction using robust lms adaptive filter. Signal Image Video Process. 15(8), 1813–1820 (2021)

    Article  Google Scholar 

  2. Yan, K., Wang, X., Kim, J., Khadra, M., Fulham, M., Feng, D.: A propagation-dnn: deep combination learning of multi-level features for mr prostate segmentation. Comput. Methods Programs Biomed. 170, 11–21 (2019)

    Article  Google Scholar 

  3. Sangaiah, A.K., Arumugam, M., Bian, G.-B.: An intelligent learning approach for improving ecg signal classification and arrhythmia analysis. Artif. Intell. Med. 103, 101788 (2020)

    Article  Google Scholar 

  4. Mousavi, S., Afghah, F., Khadem, F., Acharya, U.R.: Ecg language processing (elp): a new technique to analyze ecg signals. Comput. Methods Programs Biomed. 202, 105959 (2021)

    Article  Google Scholar 

  5. Salem, M., Taheri, S., Yuan, J.-S.: Ecg arrhythmia classification using transfer learning from 2-dimensional deep cnn features. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4 (2018). IEEE

  6. Acharya, U.R., Fujita, H., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals. Inf. Sci. 415–416, 190–198 (2017)

    Article  Google Scholar 

  7. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  8. Li, Y., Via, B.K., Li, Y.: Lifting wavelet transform for vis-nir spectral data optimization to predict wood density. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 240, 118566 (2020)

    Article  Google Scholar 

  9. Zhou, F., Li, C., Zhu, H.: Research on threshold improved denoising algorithm based on lifting wavelet transform in uv–vis spectrum. Spectrosc. Spectr. Anal. 38(2), 506–510 (2018)

    Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. Ye, C., Kumar, B.V.K.V., Coimbra, M.T.: An automatic subject-adaptable heartbeat classifier based on multiview learning. IEEE J. Biomed. Health Inform. 20(6), 1485–1492 (2016)

    Article  Google Scholar 

  12. Dewangan, R.K., Shukla, A., Godfrey, W.W.: A solution for priority-based multi-robot path planning problem with obstacles using ant lion optimization. Mod. Phys. Lett. B 34(13), 2050137 (2020)

    Article  MathSciNet  Google Scholar 

  13. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., Chen, T.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)

    Article  Google Scholar 

  14. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)

    Article  Google Scholar 

  15. Meng, D., Ge, B.: Antlion optimized adaptive routing algorithm based on cellular partition. J. Jinling Univ. Sci. Technol. 2, 17–23 (2020)

    Google Scholar 

  16. Singh, D., Singh, B.: Effective and efficient classification of gastrointestinal lesions: combining data preprocessing, feature weighting, and improved ant lion optimization. J. Ambient Intell. Hum. Comput. 1–16 (2020)

  17. Ali, E.S., Abd Elazim, S.M., Abdelaziz, A.Y.: Ant lion optimization algorithm for optimal location and sizing of renewable distributed generations. Renew. Energy 101, 1311–1324 (2017)

    Article  Google Scholar 

  18. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389–396 (2017)

    Article  Google Scholar 

  19. Mousavi, S., Afghah, F.: Inter- and intra- patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019)

  20. Shaker, A.M., Tantawi, M., Shedeed, H.A., Tolba, M.F.: Generalization of convolutional neural networks for ecg classification using generative adversarial networks. IEEE Access 8, 35592–35605 (2020)

    Article  Google Scholar 

  21. Nurmaini, S., Darmawahyuni, A., Noviar, A., Mukti, S., Tutuko, B.: Deep learning-based stacked denoising and autoencoder for ecg heartbeat classification. Electronics 9(1), 135 (2020)

    Article  Google Scholar 

  22. Oh, S.L., Ng, E.Y.K., Tan, R.S., Acharya, U.R.: Automated diagnosis of arrhythmia using combination of cnn and lstm techniques with variable length heart beats. Comput. Biol. Med. 102, 278–287 (2018)

    Article  Google Scholar 

  23. Kora, P.: Ecg based myocardial infarction detection using hybrid firefly algorithm. Comput. Methods Programs Biomed. 152, 141–148 (2017)

    Article  Google Scholar 

  24. Chen, Y., Zhang, D., Zhang, H., Wang, Q.-G.: Dual-path mixed domain residual threshold networks for bearing fault diagnosis. IEEE Trans. Ind. Electron. (2022). https://doi.org/10.1109/TIE.2022.3144572

    Article  Google Scholar 

  25. Chen, Y., Zhang, D., Karimi, H.R., Deng, C., Yin, W.: A new deep learning framework based on blood pressure range constraint for continuous cuffless bp estimation. Neural Netw. 152, 181–190 (2022)

    Article  Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wutao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamid Reza Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Dan Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent for publication

All authors agree the consent for publication when the paper is accepted.

Ethics approval and consent to participate

This article uses public datasets and does not involve ethical issues.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation