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المؤلفNazmul Islam Shuzan, Md
المؤلفHossain Chowdhury, Moajjem
المؤلفChowdhury, Muhammad E. H.
المؤلفAbualsaud, Khalid
المؤلفYaacoub, Elias
المؤلفAhasan Atick Faisal, Md
المؤلفAlshahwani, Mazun
المؤلفAl Bordeni, Noora
المؤلفAl-Kaabi, Fatima
المؤلفAl-Mohannadi, Sara
المؤلفMahmud, Sakib
المؤلفZorba, Nizar
تاريخ الإتاحة2024-07-14T07:57:20Z
تاريخ النشر2024
اسم المنشورIEEE Access
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ACCESS.2024.3404971
الرقم المعياري الدولي للكتاب21693536
معرّف المصادر الموحدhttp://hdl.handle.net/10576/56597
الملخصPatients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. Blood glucose was estimated, and diabetic severity (normal, warning, and dangerous) was stratified using Mel frequency cepstral coefficients, time, frequency, and statistical features from PPG and their derivative signals along with physiological parameters. Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. The proposed model's prediction was all in Zone A and B in the Clarke Error Grid analysis. The predictions are thus clinically acceptable. Furthermore, K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Furthermore, the proposed models were deployed in Amazon Web Server. The wristband is connected to an Android mobile application to collect real-Time data and update the estimated glucose and diabetic severity every 10-seconds, which will allow the users to gain better control of their diabetic health.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعContinuous glucose monitoring (CGM)
Internet of Things (IoT)
machine learning
photoplethysmography (PPG)
wearable device
العنوانQU-GM: An IoT Based Glucose Monitoring System from Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
النوعArticle
الصفحات77774-77790
رقم المجلد12
dc.accessType Open Access


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