Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

• URL: http://arxiv.org/abs/2102.12245v1
• Date: Wed, 24 Feb 2021 12:11:23 GMT
• Title: Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
• Authors: Eoin Brophy, Maarten De Vos, Geraldine Boylan, Tomas Ward
• Abstract summary: Ischemic heart disease is the highest cause of mortality globally each year. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and arterial pressure (BP) readings. These methods are often quite invasive, in particular when arterial pressure (ABP) readings are taken and not to mention very costly. We train our framework across distributed models and data sources to mimic a large-scale collaborative learning experiment that could be implemented across low-cost wearables.
• Score: 5.275287291173557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain 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 electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is 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 2.54 mmHg and a standard deviation of 23.7 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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