ArticleSensors (Basel, Switzerland)2022
A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals.
Article 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.
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Who cites it
15 citing papers in PubMed.
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.Scientific reports · 2025Article
- Oscillometric blood pressure estimation using machine learning-based mapping of waveform features.Biomedical engineering letters · 2025Article
- Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation.Nature reviews. Cardiology · 2025Review
- UTransBPNet for cuffless and calibration-free blood pressure estimation under dynamic conditions.Scientific reports · 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
- Optimized robust learning framework based on big data for forecasting cardiovascular crises.Scientific reports · 2024Article
- Examining the challenges of blood pressure estimation via photoplethysmogram.Scientific reports · 2024Article
- Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals.Scientific reports · 2024Article
- Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.Physical and engineering sciences in medicine · 2023Article
- Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.Sensors (Basel, Switzerland) · 2023Article
- A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.Scientific data · 2023Article
- PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms.Bioengineering (Basel, Switzerland) · 2022Article
- Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors.Sensors (Basel, Switzerland) · 2022Article
- PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods.Frontiers in digital health · 2022Article
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Authors and funding
12 authors.
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Abstract
Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
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