Key result
Cubic and Gaussian support vector machines accurately estimate BP from PPG signals meeting AAMI standards.
Why the study?
Continuous blood pressure monitoring is needed to mitigate health risks, but traditional methods like sphygmomanometers are unsuitable for continuous monitoring.
Can a machine learning-based method using photoplethysmography (PPG) signal features accurately estimate continuous blood pressure?
Population
657 PPG signals from 219 subjects
Comparison
Linear Regression vs SVM vs GPR vs Decision Tree algorithms
Design
Algorithm development and validation study
Authors
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May support cuffless BP monitoring development; leaves open prospective validation before clinical use.
Can a machine learning-based method using photoplethysmography (PPG) signal features accurately estimate continuous blood pressure?
A novel machine learning approach using PPG signals accurately estimates continuous systolic and diastolic blood pressure, meeting AAMI standards and offering a cuffless alternative for continuous monitoring.
Khan et al. (2023) studied Blood pressure estimation (n=219). Machine learning-based blood pressure estimation using PPG signals was evaluated on Systolic and diastolic blood pressure estimation accuracy. Machine learning algorithms, specifically cubic and Gaussian support vector machines, accurately estimated systolic and diastolic blood pressure from PPG signals, conforming to AAMI standards.
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