Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39303203

Abstract

Accurate non-invasive monitoring of blood glucose (BG) is a challenging issue in the therapy of diabetes. Here near-infrared (NIR) photoplethysmography (PPG) sensor based on a vapor-deposited mixed tin-lead hybrid perovskite photodetector is developed. The device shows a high detectivity of 5.32 × 10 Jones and a large linear dynamic range (LDR) of 204 dB under NIR light, guaranteeing accurate extraction of eleven features from the PPG signal. By a combination of machine learning, accurate prediction of blood glucose level with mean absolute relative difference (MARD) as small as 2.48% is realized. The self-powered PPG sensor also works for real-time outdoor healthcare monitors using sunlight as a light source. The potential for early diabetes diagnoses by the perovskite PPG sensor is demonstrated.

Authors

  • Yongjian Zheng
    Siyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong, 510632, China.
  • Zhenye Zhan
    Siyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong, 510632, China.
  • Qiulan Chen
    Chinese Center for Disease Control and Prevention, Beijing, China.
  • Jianxin Chen
    Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address: cjx@bucm.edu.cn.
  • Jianwen Luo
    Departmemt of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
  • Juntao Cai
    Guangzhou Research Institute of Optical, Mechanical and Electronical Technologies Co.,Ltd, Guangzhou, Guangdong, 510663, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Ke Chen
    Department of Signal Processing, Tampere University of Technology, Finland.
  • Weiguang Xie
    Siyuan Laboratory, Guangdong Provincial Engineering Technology Research Center of Vacuum Coating Technologies and New Energy Materials, College of Physics & Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong, 510632, China.

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