Non-invasive Assessment of Blood Glucose by Photo Plethysmography

Gd, Jindal; Ananthakrishnan, T. S.; Jain, Rajesh Kumar; Sinha, Vineet; Kini, Aarushi; Deshpande, Alaka

doi:10.1080/03772063.2008.10876202

Cited by 9 publications

(6 citation statements)

References 1 publication

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“…DVP signals are generally used to detect time domain properties (e.g., heart rate 1 , respiration rate 2 , etc.) and quantitative parameters (e.g., blood oxygenation 3 , 4 , hypovolemia and hypervolemia 5 , blood glucose level 6 , etc.) from the human body.…”

Section: Introductionmentioning

confidence: 99%

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

Hossain

Gupta

Kwon

et al. 2021

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

“…In contrast, noninvasive glucose estimation is a comparatively new topic, although some of its implementations using external bodily tissues (skin tissues) and fluids (e.g., saliva and tears) have been reported 16 , 17 . Implementations of PPG signals for blood glucose level estimation have also been presented 6 .…”

Section: Introductionmentioning

confidence: 99%

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

Hossain

Gupta

Kwon

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Cardiac function can reflect much information about the human body, including health status, daily lifestyle, emotional states, and the early onset of abnormal heart activities. In traditional medical testing equipment, the wave rates of heartbeat signals and the flow activities of cardiac blood are monitored by measuring electrophysiological signals and using an electrocardiogram (ECG) [24][25][26][27][28][29][30].…”

Section: System Architecture For Capturing Physiological Signalsmentioning

confidence: 99%

Autonomic Nerve Activation Observed for Hemodialysis Patients While Squeezing a Soft Ball

et al. 2020

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In this study, a medical grade pulse rate (PR) instrument was used to monitor hemodialysis patients, and the wearable product was applied for the 4 h observation. Electrocardiogram (ECG) and photoplethysmography (PPG) data were simultaneously collected to observe physiological phenomena in patients undergoing hemodialysis. The analyzed results of 38 patients undergoing the treatment (as sympathetic/parasympathetic balance indicators before-hemodialysis (HD), and after-HD) and autonomic nerve activation for the pulse rate (PR) measurement accompanied by squeezing a soft ball were also observed. The results prove the pulse rate measurement while squeezing the soft ball and analyze data, and we show that the analyzed results have a very concentrated normal distribution. This study presents oxygen saturation (SpO2) and continuous pulse rate distribution curves during the 4 h observation of the hemodialysis patients and we show that some patients undergoing kidney dialysis have sleep apnea. They become lethargic during dialysis and experience severe hypoxia due to intermittent respiratory arrest. Studies have confirmed that such monitoring and biofeedback designs can reduce the incidence of hypotension during dialysis.

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“…DVP signals are generally used to detect time domain properties (e.g., heart rate 1 , respiration rate 2 , etc.) and quantitative parameters (e.g., blood oxygenation 3,4 , hypo-and hypervolemia 5 , blood glucose level 6 , etc.) from the human body.…”

mentioning

confidence: 99%

“…On the contrary, non-invasive glucose estimation is a comparatively new topic, although some of its implementations using external bodily tissues (skin tissues) and fluids (e.g., saliva and tears) have been reported 16,17 . Implementations of PPG signals for the blood glucose level estimation have also been presented 6 .…”

mentioning

confidence: 99%

Derivation and Validation of Gray-Box Models to Estimate Non-Invasive In-vivo Percentage Glycated Hemoglobin Using Digital Volume Pulse Waveform

Hossain

Gupta

Kwon

et al. 2020

Preprint

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Glycated hemoglobin and blood oxygenation are the two most important factors for monitoring a patient’s oxygen levels in the blood and the amount of average blood glucose 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, while the second model is based on a whole-finger model system. We validated our two gray-box systems on diabetic and non-diabetic patients and obtained the mean absolute errors for the percent glycated hemoglobin (%HbA1c) and percent oxygen saturation (%SpO2) of 0.375 and 1.676, respectively, for the blood vessel model and 0.271 and 1.395, respectively, for the whole-finger model. The precision analysis indicated that these models resulted in 2.08% and 1.74% mean %CV for %HbA1c and 0.54% and 0.49% mean %CV for %SpO2 in the respective models. Herein, both models exhibit close performances to each other (HbA1c estimation Pearson R values are 0.92 and 0.96, respectively), even though the model assumptions greatly differed between them. Both of the models have a very high potential to be used in real-world scenarios. The whole-finger model performs better in terms of higher precision and accuracy compared to the blood vessel model.

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Non-invasive Assessment of Blood Glucose by Photo Plethysmography

Cited by 9 publications

References 1 publication

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

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

Autonomic Nerve Activation Observed for Hemodialysis Patients While Squeezing a Soft Ball

Derivation and Validation of Gray-Box Models to Estimate Non-Invasive In-vivo Percentage Glycated Hemoglobin Using Digital Volume Pulse Waveform

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