ArticleActa physiologica (Oxford, England)2025
Machine learning-based blood pressure estimation using impedance cardiography data.
Article in Acta physiologica (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads.Physiological measurement · 2026Article
- Safety and efficacy of the therapy with CD4 + CD25highCD127-T regulatory cells: When paediatric patient becomes adult.Diabetes, obesity & metabolism · 2026Article
- Which Systolic Blood Pressure Measure Is Most Important for Determining Cardiovascular Risk: Seated or Supine Blood Pressure?Current hypertension reports · 2025Review
- Machine learning-based blood pressure estimation using impedance cardiography data.Acta physiologica (Oxford, England) · 2025Article
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Abstract
objectiveAccurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort. This study is the first to access the feasibility of machine learning-based BP estimation using impedance cardiography (ICG) data.
methodsWe analyzed ICG data from 71 young and healthy adults. Nine different machine learning algorithms were evaluated for their BP estimation performance against quality controlled, oscillometric (cuff-based), arterial BP measurements during mental (Trier social stress test), and physical exercise (bike ergometer). Models were optimized to minimize the root mean squared error and their performance was evaluated against accuracy and regression metrics.
resultsThe multi-linear regression model demonstrated the highest measurement accuracy for systolic BP with a mean difference of -0.01 mmHg, a standard deviation (SD) of 10.79 mmHg, a mean absolute error (MAE) of 8.20 mmHg, and a correlation coefficient of r = 0.82. In contrast, the support vector regression model achieved the highest accuracy for diastolic BP with a mean difference of 0.15 mmHg, SD = 7.79 mmHg, MEA = 6.05 mmHg, and a correlation coefficient of r = 0.51.
conclusionThe study demonstrates the feasibility of ICG-based machine learning algorithms for estimating cuff-based reference BP. However, further research into limiting biases, improving performance, and standardized validation is needed before clinical use.
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