Abstract
Diabetes mellitus is a chronic disease characterized by producing abnormal levels of blood glucose concentration. Currently, the most widely accepted method for glucose monitoring is invasive, however, despite its great reliability, it can be uncomfortable and traumatizing for the youngest users. The objective of this study is to provide an alternative method that allows a non-invasive estimation of blood glucose levels with an elevated level of confidence. In this work, 187 records were performed on people without any declared pathology; the concentration of blood glucose and the amplitude of the PPG signals of 525 nm, 660 and 940 nm were measured simultaneously. 70% of the data was used to train a regression model based on a fine Gaussian support vector machine, while the remaining 30% is used to validate the results. The regression model using the support vector machine was able to locate 95.38% of the estimates with an error of less than 15%, showing a standard error of 7.01 mg/dL and a MARD of 6.99%. The model presented here allows non-invasive estimation of blood glucose levels with reliability comparable to minimally invasive devices currently on the market.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LA Castro-Pimentel, A del Carmen Téllez-Anguiano, OI Coronado-Reyes and JL Diaz-Huerta. The first draft of the manuscript was written by LA Castro-Pimentel and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Performed tests were authorized by the academic committee of the TecNM/Instituto Tecnológico de Morelia in accordance with the principles embodied in the Declaration of Helsinki and in accordance with local statutory requirements. Once informed of the required tests, all participants gave their written informed consent to participate in the study
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Castro-Pimentel, L.A., Téllez-Anguiano, A.d., Coronado-Reyes, O.I. et al. Three-wavelength PPG and support vector machine for non-invasive estimation of blood glucose. Opt Quant Electron 55, 708 (2023). https://doi.org/10.1007/s11082-023-04927-1
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DOI: https://doi.org/10.1007/s11082-023-04927-1