ArticleSensors (Basel, Switzerland)2021
An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 124 citations in OpenAlex.
- Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026Article
- Non-Invasive Blood Pressure Estimation Enhanced by Capillary Refill Time Modulation of PPG Signals.Sensors (Basel, Switzerland) · 2026Article
- Design and prototyping of an AI-powered wearable device for continuous vital signs monitoring with intelligent alerting.Scientific reports · 2025Article
- Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.Scientific reports · 2025Article
- A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling.Sensors (Basel, Switzerland) · 2025Article
- Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.Scientific reports · 2025Article
- Continuous noninvasive blood pressure estimation using tissue blood flow measured by diffuse correlation spectroscopy.APL bioengineering · 2025Article
- Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.Nature reviews. Cardiology · 2025Review
- Machine Learning Prediction of Mean Arterial Pressure from the Photoplethysmography Waveform During Hemorrhagic Shock and Fluid Resuscitation.Sensors (Basel, Switzerland) · 2025Article
- Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure.Bioengineering (Basel, Switzerland) · 2025Article
- A review of machine learning methods for non-invasive blood pressure estimation.Journal of clinical monitoring and computing · 2025Review
- In Vivo Evaluation of Two Hemorrhagic Shock Resuscitation Controllers with Non-Invasive, Intermittent Sensors.Bioengineering (Basel, Switzerland) · 2024Article
- Robust modelling of arterial blood pressure reconstruction from photoplethysmography.Scientific reports · 2024Article
- BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.IEEE journal of biomedical and health informatics · 2024Article
- MIMIC-BP: A curated dataset for blood pressure estimation.Scientific data · 2024Article
- SleepBP-Net: A Time-Distributed Convolutional Network for Nocturnal Blood Pressure Estimation from Photoplethysmogram.IEEE sensors journal · 2024Article
- A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.Sensors (Basel, Switzerland) · 2024Article
- Fair non-contact blood pressure estimation using imaging photoplethysmography.Biomedical optics express · 2024Article
- Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.Sensors (Basel, Switzerland) · 2023Article
- PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson's correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.
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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.