ArticleFrontiers in digital health2022
PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- The role of dataset integrity, calibration and signal quality in neural network-based blood pressure estimation.Scientific reports · 2026Article
- Systemic fragility in European total intravenous anesthesia delivery and opportunities for resilient real-time decision support.Communications medicine · 2026Review
- Personalized Calibration and Hybrid Feature Fusion for Continuous Blood Pressure Estimation Using PPG and ECG Signals.Sensors (Basel, Switzerland) · 2026Article
- A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.Machine learning. Health · 2026Article
- Cuffless hemodynamic monitoring with physics-informed machine learning models.Nature communications · 2026Article
- CardioFM: A Multimodal Foundation Model for Joint ECG and PPG Representation Learning.Research square · 2026Article
- MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.Scientific data · 2026Article
- Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals.Nature machine intelligence · 2026Article
- AI modeling photoplethysmography to electrocardiography useful for predicting cardiovascular disease.NPJ digital medicine · 2025Article
- Generalizable deep learning for photoplethysmography-based blood pressure estimation-A benchmarking study.Machine Learning. Health · 2025Article
- Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health.Communications medicine · 2025Article
- Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.Scientific reports · 2025Article
- An Ensemble-Based AI Approach for Continuous Blood Pressure Estimation in Health Monitoring Applications.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
- Multi-point sensing organic light-emitting diode display based mobile cardiovascular monitor.Nature communications · 2025Article
- Smartphone-based health monitoring in India: Data collection and evaluation for pulse rate estimation.Journal of family medicine and primary care · 2025Article
- BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.IEEE journal of biomedical and health informatics · 2024Article
- SiamQuality: a ConvNet-based foundation model for photoplethysmography signals.Physiological measurement · 2024Article
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4 authors.
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Abstract
There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents "PulseDB," the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality
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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.