ArticleScientific data2023
A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06994494 (Noninvasive Continuous BP Monitoring in Newborns Based on Pulsatile Signal Morphological Features Using NIRS), which is not on this map. Cited by 22 papers.
What it found
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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.
Noninvasive Continuous BP Monitoring in Newborns Based on Pulsatile Signal Morphological Features Using NIRS
Who cites it
22 citing papers in PubMed.
- Demographic subgroup-aware analysis of blood pressure predictive models.PLOS digital health · 2026Article
- A wearable cuffless blood pressure monitoring system: real-time measurement using force sensing and waveform analysis.Journal of clinical monitoring and computing · 2026Article
- Artificial Intelligence Enabled Diagnostics Using Photoplethysmography (PPG): Beyond SpOLife (Basel, Switzerland) · 2026Review
- Arterial blood pressure waveform reconstruction estimation from PPG using TCN-BiLSTM.American heart journal plus : cardiology research and practice · 2026Article
- A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis.Machine learning. Health · 2026Article
- From Cuffs to Code: Machine Learning in Non-Invasive Blood Pressure Monitoring.Anaesthesia, critical care & pain medicine · 2026Review
- Vascular age estimation using a consumer wearable sleep tracker.PLOS digital health · 2026Article
- Interpretable photoplethysmography-based machine-learning model for noninvasive assessment of systolic blood pressure.Frontiers in physiology · 2026Article
- Reliable wrist PPG monitoring by mitigating poor skin sensor contact.Scientific reports · 2025Article
- Generalizable deep learning for photoplethysmography-based blood pressure estimation-A benchmarking study.Machine Learning. Health · 2025Article
- Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.Scientific reports · 2025Article
- Multi-Source Causal Invariance for Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal Features.Sensors (Basel, Switzerland) · 2025Article
- Assessing the Efficacy of Various Machine Learning Algorithms in Predicting Blood Pressure Using Pulse Transit Time.Diagnostics (Basel, Switzerland) · 2025Article
- The quest for blood pressure markers in photoplethysmography and its applications in digital health.Frontiers in digital health · 2025Article
- BP-Net: Monitoring "Changes" in Blood Pressure Using PPG With Self-Contrastive Masking.IEEE journal of biomedical and health informatics · 2024Article
- Article
- LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.Physiological reports · 2024Article
- A review: Blood pressure monitoring based on PPG and circadian rhythm.APL bioengineering · 2024Review
- Challenges and prospects of visual contactless physiological monitoring in clinical study.NPJ digital medicine · 2023Review
- Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment.Biomedical optics express · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.
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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.