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Estimation of continuous blood pressure from PPG via a federated learning approach

Brophy, Eoin, De Vos, Maarten orcid logoORCID: 0000-0002-3482-5145, Boylan, Geraldine orcid logoORCID: 0000-0003-0920-5291 and Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607 (2021) Estimation of continuous blood pressure from PPG via a federated learning approach. Sensors, 21 (18). ISSN 1424-8220

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
Item Type:Article (Published)
Uncontrolled Keywords:GAN; blood pressure; photoplethysmogram; time series
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://dx.doi.org/10.3390/s21186311
Copyright Information:© 2021 The Authors. Open Access (CC-BY 4.0)
ID Code:27539
Deposited On:11 Aug 2022 15:30 by Thomas Murtagh . Last Modified 11 Aug 2022 15:30

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