Literature DB >> 34108531

Derivation and validation of gray-box models to estimate noninvasive in-vivo percentage glycated hemoglobin using digital volume pulse waveform.

Shifat Hossain1, Shantanu Sen Gupta1, Tae-Ho Kwon1, Ki-Doo Kim2.   

Abstract

Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient's average blood glucose and blood oxygen levels. Digital volume pulse acquisition is a convenient method, even for a person with no previous training or experience, can be utilized to estimate the two abovementioned physiological parameters. The physiological basis assumptions are utilized to develop two-finger models for estimating the percent glycated hemoglobin and blood oxygenation levels. The first model consists of a blood-vessel-only hypothesis, whereas the second model is based on a whole-finger model system. The two gray-box systems were validated on diabetic and nondiabetic patients. The mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) were 0.375 and 1.676 for the blood-vessel model and 0.271 and 1.395 for the whole-finger model, respectively. The repeatability analysis indicated that these models resulted in a mean percent coefficient of variation (%CV) of 2.08% and 1.74% for %HbA1c and 0.54% and 0.49% for %SpO2 in the respective models. Herein, both models exhibited similar performances (HbA1c estimation Pearson's R values were 0.92 and 0.96, respectively), despite the model assumptions differing greatly. The bias values in the Bland-Altman analysis for both models were - 0.03 ± 0.458 and - 0.063 ± 0.326 for HbA1c estimation, and 0.178 ± 2.002 and - 0.246 ± 1.69 for SpO2 estimation, respectively. Both models have a very high potential for use in real-world scenarios. The whole-finger model with a lower standard deviation in bias and higher Pearson's R value performs better in terms of higher precision and accuracy than the blood-vessel model.

Entities:  

Year:  2021        PMID: 34108531      PMCID: PMC8190179          DOI: 10.1038/s41598-021-91527-2

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  13 in total

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Review 2.  A review of variant hemoglobins interfering with hemoglobin A1c measurement.

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Journal:  IEEE Trans Biomed Eng       Date:  2017-03-01       Impact factor: 4.538

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Journal:  Trends Biotechnol       Date:  2014-05-19       Impact factor: 19.536

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Journal:  Anaesthesia       Date:  1991-03       Impact factor: 6.955

6.  A Lab-on-a-Chip-Based Non-Invasive Optical Sensor for Measuring Glucose in Saliva.

Authors:  Dong Geon Jung; Daewoong Jung; Seong Ho Kong
Journal:  Sensors (Basel)       Date:  2017-11-13       Impact factor: 3.576

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Journal:  Sensors (Basel)       Date:  2017-08-12       Impact factor: 3.576

8.  Longitudinal association of glucose metabolism with retinopathy: results from the Australian Diabetes Obesity and Lifestyle (AusDiab) study.

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Journal:  Diabetes Care       Date:  2008-04-14       Impact factor: 19.112

9.  In-vivo, non-invasive detection of hyperglycemic states in animal models using mm-wave spectroscopy.

Authors:  Pedro Martín-Mateos; Fabian Dornuf; Blanca Duarte; Bernhard Hils; Aldo Moreno-Oyervides; Oscar Elias Bonilla-Manrique; Fernando Larcher; Viktor Krozer; Pablo Acedo
Journal:  Sci Rep       Date:  2016-09-27       Impact factor: 4.379

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Authors:  Sanghamitra Mandal; M O Manasreh
Journal:  Sensors (Basel)       Date:  2018-04-04       Impact factor: 3.576

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  4 in total

Review 1.  A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform.

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Journal:  Sensors (Basel)       Date:  2022-05-23       Impact factor: 3.847

2.  Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals.

Authors:  Chowdhury Azimul Haque; Tae-Ho Kwon; Ki-Doo Kim
Journal:  Sensors (Basel)       Date:  2022-02-04       Impact factor: 3.576

3.  Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals.

Authors:  Tae-Ho Kwon; Ki-Doo Kim
Journal:  Sensors (Basel)       Date:  2022-04-12       Impact factor: 3.847

4.  Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth.

Authors:  Tasbiraha Athaya; Sunwoong Choi
Journal:  Biosensors (Basel)       Date:  2022-08-18
  4 in total

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