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논문 기본 정보

자료유형
학술저널
저자정보
Chowdhury Azimul Haque (Kookmin University) Shifat Hossain (University of Central Florida) Tae-Ho Kwon (Kookmin University) Hyoungkeun Kim (Korea I.T.S) Ki-Doo Kim (Kookmin University)
저널정보
한국통신학회 한국통신학회논문지 한국통신학회논문지 제48권 제7호
발행연도
2023.7
수록면
852 - 867 (16page)
DOI
10.7840/kics.2023.48.7.852

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초록· 키워드

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Noninvasive measurement of blood-glucose concentration can reduce both pain and complications associated with piercing the human fingertip to collect blood. Photoplethysmography (PPG) is a helpful technique that can be used to measure blood-glucose concentration without a blood sample. To facilitate such noninvasive in-vivo estimation, we propose a model based on the Beer-Lambert law for measuring blood-glucose concentration using the PPG signal. Notably, only two wavelengths are used. First, the oxygen saturation (SpO2) is estimated from the ratio of absorbance at two wavelengths, then another absorbance ratio is presented, and the blood-glucose concentration is estimated by substituting the SpO2 estimated earlier to this ratio. The PPG signals from 40 subjects were collected along with their reference blood-glucose concentrations and SpO2 values. The PPG-based blood-glucose concentrations are then calculated using mathematical equations derived from the Beer-Lambert law. A supervised machine learning model, XGBoost, is applied to calibrate the estimation model with the reference values measured using a commercial device; according to our experimental results, the Pearson correlation coefficient (Pearson’s r) value is 0.85. The proposed model based on the Beer-Lambert law thus provides a method for in-vivo estimation of blood glucose in daily applications.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Methodology
Ⅲ. Results
Ⅳ. Discussion
Ⅴ. Conclusion
References

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