Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis

Journal article

Igbe, T., Kandwal, A., Li, J., Kulwa, F., Samuel, O. and Nie, Z. 2024. Exploring EEG Signals for Noninvasive Blood Glucose Monitoring in Prediabetes Diagnosis. IEEE Transactions on Instrumentation and Measurements. 73, pp. 1-8. https://doi.org/10.1109/TIM.2024.3400333
AuthorsIgbe, T., Kandwal, A., Li, J., Kulwa, F., Samuel, O. and Nie, Z.
Abstract

Prediabetes, characterized by elevated blood glucose (BG) levels without reaching the threshold for diabetes, necessitates early detection to avert complications. Unfortunately, traditional BG monitoring methods involve painful finger pricking. Hence, exploring noninvasive alternatives for BG estimation and continuous monitoring is imperative. This article investigates electroencephalogram (EEG) frequency parameters, an underexplored aspect of prediabetes diagnosis. Our investigation involved 25 participants (17 healthy and 8 prediabetes) subjected to an oral glucose tolerance test. Continuous EEG signals were collected from three positions: frontal (F), occipital (O), and parietal (P). The analysis employed boxplots to elucidate signal patterns in three phases at 40-min equal time segments; start phase, middle phase, and end phase. The outcomes revealed compelling results: the left hemisphere’s occipital (O) recorded an impressive 90.3% and the right hemisphere’s parietal (P) exhibited a notable 90.5% change at the end phase analysis. These findings underscore the significance of EEG signal analysis for BG estimation, especially in O and P positions, where parameters, such as alpha and beta mean power (BMNP), showcase promise (P value < 0.05). Combining these EEG frequency parameters in a wearable device holds potential for healthcare and clinical solutions, facilitating noninvasive BG status estimation and prediabetes diagnosis.

KeywordsBlood glucose (BG) ; Electroencephalogram (EEG) signal; Oral glucose tolerance test (OGTT); Pattern analysis ; Prediabetes; Machine learning
Year2024
JournalIEEE Transactions on Instrumentation and Measurements
Journal citation73, pp. 1-8
PublisherIEEE
ISSN1557-9662
Digital Object Identifier (DOI)https://doi.org/10.1109/TIM.2024.3400333
Web address (URL)https://ieeexplore.ieee.org/document/10530081
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Output statusPublished
Publication dates
Online13 May 2024
Publication process dates
Accepted19 Apr 2024
Deposited22 Jul 2024
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