Beyond Cuffs and Needles: Exploring Photoplethysmography-Based Machine Learning for Blood Pressure Estimation
DOI:
https://doi.org/10.32792/jeps.v15i2.455Keywords:
Photoplethysmography, Blood Pressure, Feature extraction, Classification, Machine learningAbstract
This study explores the use of machine learning techniques to classify blood pressure (BP) based on Photoplethysmography (PPG) signals. Three popular algorithms - decision trees (DT), random forests (RF), and support vector machines (SVM) - were evaluated. The RF model outperformed the DT and SVM models with nearly 98%, 98% accuracy and F-score respectively , demonstrating its ability to capture complex non-linear relationships between PPG features and BP. It also showed robust performance in the presence of noise and variations in input PPG signals, making it a promising choice for real-world BP monitoring applications.
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