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Non-contact blood pressure detection based on weighted ensemble learning model

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Abstract

This paper proposes a non-contact method for measuring blood pressure using imaging photoplethysmography (IPPG) based on weighted ensemble learning model. The method involves recording video of the face and hand using a webcam in ambient light conditions and extracting blood pressure-related features from the IPPG signal. Machine learning methods are employed to build a blood pressure prediction model. Six machine learning algorithms were used to construct blood pressure prediction models, respectively, and the performance of the models was evaluated by Pearson’s correlation coefficient. The three machine learning algorithms with the highest correlation with the actual values were selected as base learners and input into the selected meta-learner through weight allocation. Finally, a blood pressure prediction model based on a weighted ensemble learning model was constructed. Laboratory and hospital scenarios were used to evaluate the model’s performance. In addition, this paper proposes an algorithm based on EEMD (ensemble empirical mode decomposition)–WT (wavelet transform) joint filtering to extract the IPPG signal. Finally, a non-contact blood pressure detection system was built, which is of great significance for the development of future non-contact blood pressure detection equipment.

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Availability of data and materials

The data generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Tianjin Technical Expert Project of China (Grant No. 22YDTPJC00480), Cooperative Scientific Research Program of Chunhui Projects of Ministry Education of China (Grant No. HZKY20220603), and Hebei Medical Science Research Project Plan (20211088).

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Authors and Affiliations

  1. Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Wei Chen, Dezhao Zhai, Zhu Luo, Fulong Liu, Yijing Fu & Xiaotao Zhang

  2. National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology), Tianjin, 300384, China

    Wei Chen, Dezhao Zhai, Zhu Luo, Fulong Liu, Yijing Fu & Xiaotao Zhang

  3. Systems Engineering Institute, Academy of Military Sciences, People’s Liberation Army, Tianjin, 300161, China

    Hang Wu

  4. School of Artificial Intelligence, Nankai University, Tianjin, 300381, China

    Hang Wu

  5. Department of Health Management, The First Hospital of Zhangjiakou, Zhangjiakou, 075000, Hebei Province, China

    Yan Chen

Authors
  1. Wei Chen
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  2. Dezhao Zhai
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  3. Hang Wu
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  4. Zhu Luo
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  5. Fulong Liu
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  6. Yijing Fu
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  7. Yan Chen
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  8. Xiaotao Zhang
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Contributions

WC, DZ, and HW presented the method; DZ optimized the algorithm; WC and FL conducted relevant experiments; YF and YC conducted data collection work; DZ and ZL wrote the manuscript; XZ, WC, and DZ revised and embellished the paper.

Corresponding author

Correspondence to Hang Wu.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The volunteers were informed of the specific experiment process in advance, and data collection was carried out with the consent of the volunteers.

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Chen, W., Zhai, D., Wu, H. et al. Non-contact blood pressure detection based on weighted ensemble learning model. SIViP 18, 553–560 (2024). https://doi.org/10.1007/s11760-023-02762-1

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  • DOI: https://doi.org/10.1007/s11760-023-02762-1

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