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A Health Status Evaluation Method for Chronic Disease Patients Based on Multivariate State Estimation Technique Using Wearable Physiological Signals: A Preliminary Study

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Wireless Mobile Communication and Healthcare (MobiHealth 2021)

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Abstract

Since chronic disease has become one of the most profound threats to human health, effective evaluation of human health and disease status is particularly important. In this study, we proposed a method based on Multivariate State Estimation Technique (MSET) by using physiological signals collected by a wearable device. Residual was defined as the difference between the actual value of each observed parameter and the estimated value obtained by MSET. The high-dimensional residual series were fused into a Multivariate Health Index (MHI) using a Gaussian mixture model. To preliminarily validate this method, we designed a retrospective observational study of 17 chronic patients with coronary artery disease combined high risk of heart failure whose Brain Natriuretic Peptide (BNP) had changed significantly during hospitalization. The results show that the distribution of residuals estimated by MSET had some regularity, in which the Pearson correlation coefficients between Cohen Standardized Mean Difference (SMD) and Overlapping Coefficient (OVL) of MHI and the change of BNP examination results reached 0.786 and 0.835, with their p-values less than 0.001, respectively. We preliminarily demonstrated that the model can reflect the level of change in human health status to some extent. This MSET-based approach shows great potential for applications of treatment effect evaluation, and provides abundant information from physiological signals in chronic disease management.

Haoran Xu and Zhicheng Yang—Equally contributed to this work.

This work was done during Zhicheng Yang’s internship at Beijing SensEcho Science & Technology Co., Ltd., Beijing, China, when he was a Ph.D. candidate at University of California, Davis, CA, USA.

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Notes

  1. 1.

    BNP change is computed by subtracting the second BNP examination result from the first one.

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Acknowledgment

This work is supported by The National Natural Science Foundation of China (62171471); Beijing Municipal Science and Technology (Z181100001918023); Big Data Research & Development Project of Chinese PLA General Hospital (2018MBD-09).

Author information

Authors and Affiliations

  1. Medical School of Chinese PLA, Beijing, China

    Haoran Xu, Zhao Wang & Jiachen Wang

  2. Affiliated Hospital of Medical Sergeant School, Army Medical University, Shijiazhuang, Hebei, China

    Haoran Xu

  3. PAII Inc., Palo Alto, CA, USA

    Zhicheng Yang

  4. Beijing SensEcho Science & Technology Co., Ltd., Beijing, China

    Ke Lan

  5. Department of Hyperbaric Oxygen Therapy, The First Medical Center, Chinese PLA General Hospital, Beijing, China

    Wei Yan & Muyang Yan

  6. Shanghai University of Sport, Shanghai, China

    Yaning Zang

  7. University of Missouri - St. Louis, St. Louis, MO, USA

    Jianli Pan

  8. Center for Artificial Intelligence in Medicine, Chinese PLA General Hospital, Beijing, China

    Zhengbo Zhang

Authors
  1. Haoran Xu
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  2. Zhicheng Yang
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  3. Ke Lan
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  4. Wei Yan
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Corresponding authors

Correspondence to Muyang Yan or Zhengbo Zhang .

Editor information

Editors and Affiliations

  1. Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinbo Gao

  2. The University of Sydney, Sydney, NSW, Australia

    Abbas Jamalipour

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Lei Guo

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xu, H. et al. (2022). A Health Status Evaluation Method for Chronic Disease Patients Based on Multivariate State Estimation Technique Using Wearable Physiological Signals: A Preliminary Study. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-06368-8_1

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