ReviewSensors (Basel, Switzerland)2022
Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.
Review 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 15 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
15 citing papers in PubMed, 42 citations in OpenAlex.
- Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- PPG-Net 4: Deep-Learning-Based Approach for Classification of Blood Flow Using Non-Invasive Dual Photoplethysmography (PPG) Signals.Sensors (Basel, Switzerland) · 2025Article
- A Novel Wearable Device for Continuous Blood Pressure Monitoring Utilizing Strain Gauge Technology.Biosensors · 2025Article
- Cuffless Blood Pressure Measurement: Where Do We Actually Stand?Hypertension (Dallas, Tex. : 1979) · 2025Review
- Noninvasive continuous blood pressure prediction using FlexNIRS and machine learning during carotid endarterectomy.Journal of biomedical optics · 2025Article
- Assessing the Efficacy of Various Machine Learning Algorithms in Predicting Blood Pressure Using Pulse Transit Time.Diagnostics (Basel, Switzerland) · 2025Article
- Systolic Pressure and Pulse Rate Range Performance Comparison of Seven Non-Invasive Blood Pressure Monitors.Medical devices (Auckland, N.Z.) · 2025Article
- Artificial Intelligence-Based Diagnostic Support System for Patent Ductus Arteriosus in Premature Infants.Journal of clinical medicine · 2024Article
- Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.Physical and engineering sciences in medicine · 2023Article
- Automatic Calibration of a Device for Blood Pressure Waveform Measurement.Sensors (Basel, Switzerland) · 2023Article
- Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer.Sensors (Basel, Switzerland) · 2023Article
- Continuous Blood Pressure Monitoring in Patients Having Surgery: A Narrative Review.Medicina (Kaunas, Lithuania) · 2023Review
- Wearable Two-Dimensional Nanomaterial-Based Flexible Sensors for Blood Pressure Monitoring: A Review.Nanomaterials (Basel, Switzerland) · 2023Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.
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.