ArticleDiagnostics (Basel, Switzerland)2023
A Novel Cuffless Blood Pressure Prediction: Uncovering New Features and New Hybrid ML Models.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Evaluating a Smartphone App to Monitor Blood Pressure in Normotensive Pregnancies, High-Risk Pregnancies, and Women With Preeclampsia: Prospective Longitudinal Feasibility Study.JMIR human factors · 2026Article
- 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
- Non-invasive blood pressure monitoring with in-ear infrasonic hemodynography for preventative cardiovascular care.NPJ cardiovascular health · 2025Article
- Integrated wearable PPG: a multi-vital sign monitoring based on group sparse mode decomposition framework in remote health care using PPG signal.Physical and engineering sciences in medicine · 2025Article
- Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.Physical and engineering sciences in medicine · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
This paper investigates new feature extraction and regression methods for predicting cuffless blood pressure from PPG signals. Cuffless blood pressure is a technology that measures blood pressure without needing a cuff. This technology can be used in various medical applications, including home health monitoring, clinical uses, and portable devices. The new feature extraction method involves extracting meaningful features (time and chaotic features) from the PPG signals in the prediction of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. These extracted features are then used as inputs to regression models, which are used to predict cuffless blood pressure. The regression model performances were evaluated using root mean squared error (RMSE), R
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Registered trials
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