ArticleFrontiers in physiology2023
Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied.
Article in Frontiers in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Towards trustworthy AI-driven cuffless blood pressure monitoring.NPJ digital medicine · 2026Article
- Evaluation of physiology-based models for noninvasive blood pressure estimation using pulse arrival time.Scientific reports · 2026Article
- Evaluating cross-dataset transfer learning for photoplethysmography-based blood pressure estimation.Scientific reports · 2026Article
- Machine Learning Insights into Age-Related Sex Differences in Blood Pressure: Influence of Diabetes and Body Mass Index.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2026Article
- Generalizable deep learning for photoplethysmography-based blood pressure estimation-A benchmarking study.Machine Learning. Health · 2025Article
- Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives.Physical and engineering sciences in medicine · 2025Article
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Abstract
The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions. The distribution of systolic and diastolic BP data and 31 PPG pulse morphological features was first compared between datasets. Then, the correlation between features and BP, as well as between the features themselves, was analysed. Finally, regression models were trained for each dataset and validated against the other. Statistical analysis showed significant
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