Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using Metaheuristic Algorithms

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature...

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Veröffentlicht in:	Algorithms Jg. 18; H. 2; S. 95
Hauptverfasser:	Alghlayini, Saifeddin, Al-Betar, Mohammed Azmi, Atef, Mohamed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.02.2025
Schlagworte:	Accuracy Algorithms Blood blood glucose level (BGL) Blood pressure Blood sugar Blood sugar monitoring Cardiovascular disease Datasets Diabetes Effectiveness Electrocardiography Feature extraction Feature selection feature selection (FS) Genetic algorithms Glucose Glucose monitoring Hawks Heart beat Heart rate heart rate variability (HRV) Heuristic methods Hyperglycemia Machine learning metaheuristic algorithms Monitoring Neural networks non-invasive photoplethysmography (PPG) Root-mean-square errors Sensors Smartphones Type 2 diabetes
ISSN:	1999-4893, 1999-4893
Online-Zugang:	Volltext
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