  Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure
(BP) measurement is interesting for various reasons. First, PPG can easily be
measured using fingerclip sensors. Second, camera-based approaches allow to
derive remote PPG (rPPG) signals similar to PPG and therefore provide the
opportunity for non-invasive measurements of BP. Various methods relying on
machine learning techniques have recently been published. Performances are
often reported as the mean average error (MAE) on the data which is
problematic. This work aims to analyze the PPG- and rPPG-based BP prediction
error with respect to the underlying data distribution. First, we train
established neural network (NN) architectures and derive an appropriate
parameterization of input segments drawn from continuous PPG signals. Second,
we apply this parameterization to a larger PPG dataset and train NNs to predict
BP. The resulting prediction errors increase towards less frequent BP values.
Third, we use transfer learning to train the NNs for rPPG based BP prediction.
The resulting performances are similar to the PPG-only case. Finally, we apply
a personalization technique and retrain our NNs with subject-specific data.
This slightly reduces the prediction errors.
