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Continuous Blood Pressure Estimation Using PPG and ECG Signal

  • Bo Wang
  • Zhipei HuangEmail author
  • Jiankang Wu
  • Zhongdi Liu
  • Yuanyuan Liu
  • Pengjie Zhang
Conference paper
Part of the Internet of Things book series (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.

Keywords

Photoplethysmography Electrocardiogram Continuous blood pressure Artificial neural network Genetic algorithm 

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bo Wang
    • 1
  • Zhipei Huang
    • 1
    Email author
  • Jiankang Wu
    • 1
  • Zhongdi Liu
    • 1
  • Yuanyuan Liu
    • 1
  • Pengjie Zhang
    • 1
  1. 1.University of Chinese Academy of SciencesBeijingChina

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