A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

Journal: Computer methods and programs in biomedicine
PMID: 40158260

Abstract

BACKGROUND AND OBJECTIVE: The aim of this study is to extract a patient-specific viscosity equation from photoplethysmography (PPG) data. An aging society has increased the need for remote, non-invasive health monitoring systems. However, the circulatory system remains beyond the scope of wearable devices. The solution might be found in the possibility of measuring blood viscosity from wearable devices. Blood viscosity information can be used to monitor and diagnose various circulatory system diseases. Therefore, if blood viscosity can be calculated from wearable photoplethysmography, the versatility of a non-invasive health monitoring system can be broadened.

Authors

  • Hyeong Jun Lee
    Division of Biomarkers, Imaging, and Hemodynamic Studies (BIOS), Department of Mechanical Engineering, Yonsei University, Seoul, Korea; Center for Precision Medicine Platform Based on Smart Hemo-Dynamic Index (SHDI), Seoul, Korea.
  • Young Woo Kim
    College of Korean Medicine, Dongguk University, 32 Dongguk-ro, Ilsandong-gu, Goyang-si 10326, Korea.
  • Seung Yong Shin
    Department of Internal Medicine, College of Medicine, Chung-Ang University, Seoul 06973, Republic of Korea.
  • San Lee Lee
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Chae Hyeon Kim
    Department of Medical Devices Industry, Dongguk University-Seoul, Seoul, Korea.
  • Kyung Soo Chung
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. Electronic address: CHUNGKS@yuhs.ac.
  • Joon Sang Lee
    Department of Pediatrics, National Police Hospital, Seoul, Korea.

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