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Continuous Blood Pressure Estimation Using PPG and ECG Signal

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Advances in Body Area Networks I

Part of the book series: Internet of Things ((ITTCC))

Abstract

Continuous blood pressure monitor can detect the potential risk of cardiovascular disease and provide a gold standard for clinical diagnosis. The features extracted from photoplethysmography (PPG) and electrocardiogram (ECG) signals can reflect the dynamics of cardiovascular system. In this paper, 39 features are extracted from PPG and ECG signals and 10 features are chosen by analyzing their correlations with blood pressure. Several machine learning algorithms are used to predict the continuous and cuff-less estimation of the diastolic blood pressure and systolic blood pressure. The results shows that compared with linear regression and support vector regression methods, the artificial neural network optimized by genetic algorithm gives a better accuracy for 1 h prediction under Advancement of Medical Instrumentation and the British Hypertension Society standard.

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Acknowledgements

This work was supported by Special Fund for Scientific Research Cooperation of University Chinese Academy of Sciences.

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Bo Wang, Zhipei Huang, Jiankang Wu, Zhongdi Liu, Yuanyuan Liu & Pengjie Zhang

Authors
  1. Bo Wang
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  2. Zhipei Huang
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  3. Jiankang Wu
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  4. Zhongdi Liu
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  5. Yuanyuan Liu
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  6. Pengjie Zhang
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Corresponding author

Correspondence to Zhipei Huang .

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Editors and Affiliations

  1. DIMES, Cubo 41C, Universita della Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, Liaoning, China

    Zhelong Wang

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Wang, B., Huang, Z., Wu, J., Liu, Z., Liu, Y., Zhang, P. (2019). Continuous Blood Pressure Estimation Using PPG and ECG Signal. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-02819-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02818-3

  • Online ISBN: 978-3-030-02819-0

  • eBook Packages: EngineeringEngineering (R0)

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