ArticleSensors (Basel, Switzerland)2022
Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Machine Learning Methods for the Prediction of Intraoperative Hypotension with Biosignal Waveforms.Medicina (Kaunas, Lithuania) · 2025Observational
- Robust Estimation of Unsteady Beat-to-Beat Systolic Blood Pressure Trends Using Photoplethysmography Contextual Cycles.Sensors (Basel, Switzerland) · 2025Article
- Recommendations for evaluating photoplethysmography-based algorithms for blood pressure assessment.Communications medicine · 2024Review
- Recent developments in machine learning modeling methods for hypertension treatment.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Review
- Article
- A prototype photoplethysmography-based cuffless device shows promising results in tracking changes in blood pressure.Frontiers in medical technology · 2024Article
- Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters.Entropy (Basel, Switzerland) · 2023Article
- Non-Invasive Blood Pressure Sensing via Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Beat-to-beat finger photoplethysmography in atrial fibrillation patients undergoing electrical cardioversion.Scientific reports · 2023Article
- Photoplethysmography Driven Hypertension Identification: A Pilot Study.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.