Publication: การพัฒนาระบบประมาณระดับกลูโคสในเลือดแบบไม่รุกล้ำ
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Issued Date
2022
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Language
tha
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application/pdf
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open access
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ผลงานนี้เผยแพร่ภายใต้ สัญญาอนุญาตครีเอทีฟคอมมอนส์แบบ แสดงที่มา-ไม่ใช้เพื่อการค้า-ไม่ดัดแปลง 4.0 (CC BY-NC-ND 4.0)
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Suggested Citation
ศุภนิดา ปัทมา, จิดาภา โตสวัสดิ์ (2022). การพัฒนาระบบประมาณระดับกลูโคสในเลือดแบบไม่รุกล้ำ. สืบค้นจาก: https://hdl.handle.net/20.500.14740/10713
Alternative Title(s)
The development of a non-invasive blood glucose estimation system
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Advisor(s)
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Abstract
Researchers have been working on designing and developing a device and method
for non-invasive blood glucose measurement that uses optical detection. The device
operates by emitting light energy at near-infrared radiation and near-red wavelengths
through areas of the body capable of detecting the contraction of PPG blood vessels, such
as the fingertips. This non-invasive approach to monitoring blood glucose levels is crucial
for individuals at risk of or suffering from diabetes or obesity, as it allows them to track
their blood glucose levels and receive appropriate treatment to maintain equilibrium. This
contrasts with current invasive measurement methods, such as self-monitoring blood
glucose, which requires a small needle to puncture the fingertip and collect a blood
sample. For individuals who need to measure their blood glucose levels daily, repeated
punctures can result in bodily injuries and larger wounds. The researchers’ technique
estimates blood glucose levels by analyzing characteristic signals obtained from the
contractile signal collection in a time-series manner. They found a correlation between
blood glucose data and 1 0 features, including heart rate signals, ac and de components,
perfusion index, and the ratio of the perfusion index between infrared and red-light signals.
The researchers created a blood glucose estimation model using a Polynomial
Regression model, based on the most correlated characteristics of the ac component and
perfusion index of the red-light signal. They tested the 1 st, 2nd, and 3rd derivatives in 2
participants over a period of 1 0 days, with each participant providing data for 5 days. The
blood glucose test was divided into two parts: the first part after glucose intake and theง
second part after eating, while controlling the subject’s diet. The first-order differential
regression model was found to be the most accurate, with an accuracy of 99.78% for the
red ac component model and 99.77% for the red-light perfusion index model.