ArticleSensors (Basel, Switzerland)2021
Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism.
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 30 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
30 citing papers in PubMed.
- From Cuffs to Code: Machine Learning in Non-Invasive Blood Pressure Monitoring.Anaesthesia, critical care & pain medicine · 2026Review
- Non-Invasive Blood Pressure Estimation Enhanced by Capillary Refill Time Modulation of PPG Signals.Sensors (Basel, Switzerland) · 2026Article
- A 1-Dimensional Physiological Signal Prediction Method Based on Composite Feature Preprocessing and Multi-Scale Modeling.Sensors (Basel, Switzerland) · 2025Article
- Continuous noninvasive blood pressure estimation using tissue blood flow measured by diffuse correlation spectroscopy.APL bioengineering · 2025Article
- A Two-Branch ResNet-BiLSTM Deep Learning Framework for Extracting Multimodal Features Applied to PPG-Based Cuffless Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2025Article
- Multi-Source Causal Invariance for Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal Features.Sensors (Basel, Switzerland) · 2025Article
- Assessing the acute effect of compression socks on improving arterial compliance in young volunteers.Heliyon · 2025Article
- 1DCNN-BiLSTM-transformer hypertension risk prediction model based on APW.Frontiers in microbiology · 2025Article
- BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.IEEE journal of biomedical and health informatics · 2024Article
- Photoplethysmography-based non-invasive blood pressure monitoring via ensemble model and imbalanced dataset processing.Physical and engineering sciences in medicine · 2024Article
- Application of Real-Time Palm Imaging with Nelder-Mead Particle Swarm Optimization/Regression Algorithms for Non-Contact Blood Pressure Detection.Biomimetics (Basel, Switzerland) · 2024Article
- Application of Independent Component Analysis and Nelder-Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation.Sensors (Basel, Switzerland) · 2024Article
- Continuous Patient-Independent Estimation of Respiratory Rate and Blood Pressure Using Robust Spectro-Temporal Features Derived From Photoplethysmogram Only.IEEE open journal of engineering in medicine and biology · 2024Article
- PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.Diagnostics (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
- A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.Scientific data · 2023Article
- Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.Biosensors · 2023Article
- Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.Sensors (Basel, Switzerland) · 2023Article
- Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021.Yearbook of medical informatics · 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.
Funding
Abstract
Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.
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.