  Electrocardiograms (ECGs) and photoplethysmograms (PPGs) are generally used
to monitor an individual's cardiovascular health. In clinical settings, ECGs
and fingertip PPGs are the main signals used for assessing cardiovascular
health, but the equipment necessary for their collection precludes their use in
daily monitoring. Although PPGs obtained from wrist-worn devices are
susceptible to noise due to motion, they have been widely used to continuously
monitor cardiovascular health because of their convenience. Therefore, we would
like to combine the ease with which PPGs can be collected with the information
that ECGs provide about cardiovascular health by developing models to
synthesize ECG signals from paired PPG signals. We tackled this problem using
generative adversarial networks (GANs) and found that models trained using the
original GAN formulations can be successfully used to synthesize ECG signals
from which heart rate can be extracted using standard signal processing
pipelines. Incorporating a frequency-domain constraint to model training
improved the stability of model performance and also the performance on heart
rate estimation.
