ArticleSensors (Basel, Switzerland)2018
Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 158 citations in OpenAlex.
- Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation.IEEE transactions on bio-medical engineering · 2024Pooled it
- A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis.Sensors (Basel, Switzerland) · 2020Pooled it
- Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review.Frontiers in medicine · 2020Pooled it
- Personalized Calibration and Hybrid Feature Fusion for Continuous Blood Pressure Estimation Using PPG and ECG Signals.Sensors (Basel, Switzerland) · 2026Article
- Towards trustworthy AI-driven cuffless blood pressure monitoring.NPJ digital medicine · 2026Article
- Ion-Driven Interfacial Engineering of MXene-Polyacrylamide Hydrogels for Advanced Wearable Electrocardiography and AI-Driven Blood Pressure Monitoring.Small science · 2026Article
- Beyond the Cuff: State-of-the-Art on Cuffless Blood Pressure Monitoring.Sensors (Basel, Switzerland) · 2026Review
- Estimating blood pressure from the electrocardiogram: findings of a large-scale negative results study.Physiological measurement · 2025Article
- Forecasting intraoperative hypotension during hepatobiliary surgery.Journal of clinical monitoring and computing · 2025Article
- Machine learning based, subject-specific, gender and race independent, non-invasive estimation of the arterial blood pressure.NPJ cardiovascular health · 2025Article
- Detection of impending reflex syncope by means of an integrated multisensor patch-type recorder.Journal of human hypertension · 2023Article
- Multitask Attention-Based Neural Network for Intraoperative Hypotension Prediction.Bioengineering (Basel, Switzerland) · 2023Article
- Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.NPJ digital medicine · 2023Review
- Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor.Biomedical engineering letters · 2023Article
- Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT).Sensors (Basel, Switzerland) · 2023Article
- Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure.Scientific reports · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.Sensors (Basel, Switzerland) · 2022Review
- Multiparametric cloth-based wearable, SimpleSense, estimates blood pressure.Scientific reports · 2022Article
- Big Data in electrophysiology.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
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 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBlood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals.
methodsRaw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user.
resultsUsing ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP.
conclusionThe experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.
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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.