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EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms

التفاصيل البيبلوغرافية
العنوان: EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
المؤلفون: Shama Satter, Mrinmoy Sarker Turja, Tae-Ho Kwon, Ki-Doo Kim
المصدر: Applied Sciences, Vol 14, Iss 4, p 1406 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: photoplethysmography, blood glucose, diabetes, machine learning, empirical mode decomposition, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Effective management of diabetes requires accurate monitoring of blood glucose levels. Traditional invasive methods for such monitoring can be cumbersome and uncomfortable for patients. In this study, we introduce a noninvasive approach to estimate blood glucose levels using photoplethysmography (PPG) signals. We have focused on blood glucose prediction using wrist PPG signals and explored various PPG waveform-based features, including AC to DC ratio (AC/DC) and intrinsic mode function (IMF)-based features derived from empirical mode decomposition (EMD). To the best of our knowledge, no studies have been found using EMD-based features to estimate blood glucose levels noninvasively. Additionally, feature importance-based selection has also been used to further improve the accuracy of the proposed model. Among the four machine learning algorithms considered in this study, CatBoost consistently outperformed XGBoost, LightGBM, and random forest across a wide number of features. The best performing model, CatBoost, achieved Pearson’s r of 0.96, MSE 0.08, R2 score 0.92, and MAE 8.01 when considering the top 50 features selected from both PPG waveform-based features and IMF-based features. The p-values for all models were
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/14/4/1406; https://doaj.org/toc/2076-3417
DOI: 10.3390/app14041406
URL الوصول: https://doaj.org/article/22de7a8fa4a94e03a88f9ba8433ad0d1
رقم الأكسشن: edsdoj.22de7a8fa4a94e03a88f9ba8433ad0d1
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app14041406