  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.
