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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Bibliographic Details
Published in:Algorithms Vol. 18; no. 2; p. 95
Main Authors: Alghlayini, Saifeddin, Al-Betar, Mohammed Azmi, Atef, Mohamed
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2025
Subjects:
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 Access:Get full text
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Summary: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 selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18020095