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Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia

  • S.I.: AI-based e-diagnosis
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Abstract

Feature extraction plays an important role in arrhythmia classification, and successful arrhythmia classification generally depends on ECG feature extraction. This paper proposed a feature extraction method combining traditional approaches and 1D-CNN aiming to find the optimal feature set to improve the accuracy of arrhythmia classification. The proposed method is verified by using the MIT-BIH arrhythmia benchmark database. It is found that the features extracted by 1D-CNN and discrete wavelet transform form the optimal feature set with the average classification accuracy up to 98.35%, which is better than the latest methods.

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Acknowledgements

This work was supported by the King Saud University, Riyadh, Saudi Arabia through Researchers Supporting Project number RSP-2021/18. The work was also supported by the Xiamen science and technology project (3502Z20183047), China Education and research network (NGII20160201), China Postdoctoral Science Foundation (2018M643256), the Fund Project of science and technology development center of the Ministry of Education(2018A01032).

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Authors and Affiliations

  1. School of Software Engineering, Xiamen University of Technology, Xiamen, China

    Jianfeng Cui

  2. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

    Lixin Wang & Xiangmin He

  3. Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal

    Victor Hugo C. De Albuquerque

  4. Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Salman A. AlQahtani

  5. Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mohammad Mehedi Hassan

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  1. Jianfeng Cui
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  2. Lixin Wang
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  4. Victor Hugo C. De Albuquerque
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Correspondence to Jianfeng Cui or Mohammad Mehedi Hassan.

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Cui, J., Wang, L., He, X. et al. Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia. Neural Comput & Applic 35, 16073–16087 (2023). https://doi.org/10.1007/s00521-021-06487-5

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