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MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis

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Abstract

Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (SNR) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.

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Data availability

The codes are available online at https://github.com/PHD-Fang/MEAs-Filter.

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Funding

This work is supported by the Ministry of Science and Technology of the People's Republic of China (STI2030-Major Projects2021ZD0201900), National Natural Science Foundation of China (Grant No. 12090052), Foundation of Education Department of Liaoning Province (Grant No. LJKZ0280), Natural Science Foundation of Liaoning Province (Grant No. 2023-MS-288), Fundamental Research Funds for the Central Universities (Grant No. 20720230017), and Basic Public Welfare Research Program of Zhejiang Province (Grant No. LGF20F030005).

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Authors and Affiliations

  1. Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China

    Fangfang Zhu & Xiang Li

  2. National Institute for Data Science in Health and Medicine, and State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005, China

    Fangfang Zhu

  3. Yangtze Delta Region Institute of Tsinghua University, Zhejiang, Jiaxing, 314006, China

    Ji Ding & Honghong Su

  4. Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China

    Yuer Lu & Jianwei Shuai

  5. School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Xiao Liu & Frank Jiang

  6. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

    Qi Zhao

Authors
  1. Fangfang Zhu
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  2. Ji Ding
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  3. Xiang Li
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  4. Yuer Lu
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  5. Xiao Liu
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  6. Frank Jiang
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  7. Qi Zhao
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  8. Honghong Su
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  9. Jianwei Shuai
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Corresponding authors

Correspondence to Qi Zhao, Honghong Su or Jianwei Shuai.

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Zhu, F., Ding, J., Li, X. et al. MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis. Health Inf Sci Syst 12, 8 (2024). https://doi.org/10.1007/s13755-023-00268-1

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