Modified lambert beer for bilirubin concentration and blood oxygen saturation prediction

International Journal of Advances in Intelligent Informatics

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Title Modified lambert beer for bilirubin concentration and blood oxygen saturation prediction
Creator Ong, Pek Ek
Huong, Audrey Kah Ching
Ngu, Xavier Toh Ik
Mahmud, Farhanahani
Philimon, Sheena Punai
Subject Bilirubin; Blood oxygen saturation; Modified lambert beer law; Raytracing
Description Noninvasive measurement of health parameters such as blood oxygen saturation and bilirubin concentration predicted via an appropriate light reflectance model based on the measured optical signals is of eminent interest in biomedical research. This is to replace the use of conventional invasive blood sampling approach. This study aims to investigate the feasibility of using Modified Lambert Beer model (MLB) in the prediction of one’s bilirubin concentration and blood oxygen saturation value, SO2. This quantification technique is based on a priori knowledge of extinction coefficients of bilirubin and hemoglobin derivatives in the wavelength range of 440 – 500 nm. The validity of the prediction was evaluated using light reflectance data from TracePro raytracing software for a single-layered skin model with varying bilirubin concentration. The results revealed some promising trends in the estimated bilirubin concentration with mean ± standard deviation (SD) error of 0.255 ± 0.025 g/l. Meanwhile, a remarkable low mean ± SD error of 9.11 ± 2.48 % was found for the predicted SO2 value. It was concluded that these errors are likely due to the insufficiency of the MLB at describing changes in the light attenuation with the underlying light absorption processes. In addition, this study also suggested the use of a linear regression model deduced from this work for an improved prediction of the required health parameter values.
Publisher Universitas Ahmad Dahlan
Date 2019-07-26
Type info:eu-repo/semantics/article

Format application/pdf
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 2 (2019): July 2019; 113-122
Language eng

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