Second Derivative and Contour Analysis of PPG for Diabetic Patients

Usman, Sahnius; Bani, Nurul Aini; Kaidi, Hazilah Mad; Aris, Siti Armiza Mohd; Jalil, Siti Zura A.; Muhtazaruddin, Mohd Nabil

doi:10.1109/iecbes.2018.8626681

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Growing evidences suggest bidirectional association between the diabetes and depression, which environmental and genetic factors may mediate and may have shared underlying biological mechanisms [10]. Diabetes and depression are related in neuroendocrinology, neuro biochemistry and neuron electrophysiology.…”

Section: Mechanisms Of the Comorbidity Of Diabetesmentioning

confidence: 99%

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

Chaitanya Kulkarni

2023

IJRITCC

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To break down the frequency and related risk elements of sorrow in patients with type 2 diabetes mellitus in the nearby local area and give logical references to clinical anticipation and treatment of diabetes mellitus with wretchedness. Proposed strategies use AI more than 58 patients with type 2 diabetes mellitus were chosen by efficient inspecting, and pertinent surveys examined segment factors and related clinical variables and misery sub-scale (PHQ) was utilized to assess the level of sadness. Social help scale was utilized to survey the patients. SSRS are utilized to assess individual social help levels and lead measurable investigation. After assessment it's seen that among the 58 patients with type 2 diabetes, the rate of consolidated melancholy was 58%; the age, conjugal status, training level, occupation, family ancestry, term of diabetes, entanglements, work out. The distinction in friendly help was genuinely critical. The impacting elements of type 2 diabetes confounded with gloom incorporate age, conjugal status, training level, occupation, and family ancestry, span of diabetes, presence or nonappearance of confusions, exercise and social help. They have a high gamble of muddled sorrow and influence the improvement of diabetes.

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Section: Mechanisms Of the Comorbidity Of Diabetesmentioning

confidence: 99%

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

Chaitanya Kulkarni

2023

IJRITCC

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“…To represent Eqs. (20) and (21) with the %SpO 2 and %HbA1c terms, the equations can be simplified with 1 , , and terms from Eqs. (8)- (10).…”

Section: A Blood Vessel Modelmentioning

confidence: 99%

“…One research discussed the estimation of in-vitro HbA1c 20 , but only focused on the PPG sensor design and did not cover non-invasive in-vivo estimation methods. And there are other conference papers, which also focuses on the classification of a person's diabetic status, but not estimation of glycated hemoglobin 21,22 . And another paper focuses on breath acetone-based HbA1c estimation 23 , but the error rate is very high.…”

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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“…Another group of researchers classified mice models into diabetic, obese, and normal control groups, based on the measurement conditions associated with hyperglycemia [ 7 ]. Some other studies reported the classification into diabetic and non-diabetic statuses for subjects [ 8 ]. The HbA1c levels were also estimated using the acetone levels in the breath in another study [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems

Hossain

Kim

2023

Sensors

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The early diagnosis of diabetes mellitus in normal people or maintaining stable blood sugar concentrations in diabetic patients requires frequent monitoring of the blood sugar levels. However, regular monitoring of the sugar levels is problematic owing to the pain and inconvenience associated with pricking the fingertip or using minimally invasive patches. In this study, we devise a noninvasive method to estimate the percentage of the in vivo glycated hemoglobin (HbA1c) values from Monte Carlo photon propagation simulations, based on models of the wrist using 3D magnetic resonance (MR) image data. The MR image slices are first segmented for several different tissue types, and the proposed Monte Carlo photon propagation system with complex composite tissue support is then used to derive several models for the fingertip and wrist sections with different wavelengths of light sources and photodetector arrangements. The Pearson r values for the estimated percent HbA1c values are 0.94 and 0.96 for the fingertip transmission- and reflection-type measurements, respectively. This is found to be the best among the related studies. Furthermore, a single-detector multiple-source arrangement resulted in a Pearson r value of 0.97 for the wrist. The Bland–Altman bias values were found to be −0.003 ± 0.36, 0.01 ± 0.25, and 0.01 ± 0.21, for the two fingertip and wrist models, respectively, which conform to the standards of the current state-of-the-art invasive point-of-care devices. The implementation of these algorithms will be a suitable alternative to the invasive state-of-the-art methods.

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Second Derivative and Contour Analysis of PPG for Diabetic Patients

Cited by 8 publications

References 16 publications

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

Medical Estimating PF Machine Learning and IoT in Melancholy among Diabetic Patients

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

Non-Invasive In Vivo Estimation of HbA1c Using Monte Carlo Photon Propagation Simulation: Application of Tissue-Segmented 3D MRI Stacks of the Fingertip and Wrist for Wearable Systems

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