ArticleSensors (Basel, Switzerland)2021
Estimation of Continuous Blood Pressure from PPG via a Federated Learning 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 11 papers.
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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.
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Who cites it
11 citing papers in PubMed.
- Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Feature extraction tool using temporal landmarks in arterial blood pressure and photoplethysmography waveforms.NPJ cardiovascular health · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- Blood Pressure Estimation Based on PPG and ECG Signals Using Knowledge Distillation.Cardiovascular engineering and technology · 2024Article
- Reviewing Federated Machine Learning and Its Use in Diseases Prediction.Sensors (Basel, Switzerland) · 2023Review
- Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach.Sensors (Basel, Switzerland) · 2023Article
- Exploiting moving slope features of PPG derivatives for estimation of mean arterial pressure.Biomedical engineering letters · 2023Article
- Federated Learning in Ocular Imaging: Current Progress and Future Direction.Diagnostics (Basel, Switzerland) · 2022Review
- Noninvasive blood oxygen, heartbeat rate, and blood pressure parameter monitoring by photoplethysmography signals.Heliyon · 2022Article
- Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring.Healthcare (Basel, Switzerland) · 2022Review
- Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.BioMed research international · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
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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.