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Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure(BP) measurement is interesting for various reasons. First, PPG can easily bemeasured using fingerclip sensors. Second, camera-based approaches allow toderive remote PPG (rPPG) signals similar to PPG and therefore provide theopportunity for non-invasive measurements of BP. Various methods relying onmachine learning techniques have recently been published. Performances areoften reported as the mean average error (MAE) on the data which isproblematic. This work aims to analyze the PPG- and rPPG-based BP predictionerror with respect to the underlying data distribution. First, we trainestablished neural network (NN) architectures and derive an appropriateparameterization of input segments drawn from continuous PPG signals. Second,we apply this parameterization to a larger PPG dataset and train NNs to predictBP. 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 applya personalization technique and retrain our NNs with subject-specific data.This slightly reduces the prediction errors.

Assessment of deep learning based blood pressure prediction from PPG and rPPG signals