Figure 1
From: Examining the challenges of blood pressure estimation via photoplethysmogram
When designing end-to-end machine learning models researchers often use techniques such as: (A) providing the model with observations from similar patients, (B) constraining the task (e.g., limiting the distribution of labels), (C) calibrating models using data from a participant. When doing so it can often be difficult to identify how these steps impact the integrity of a model, or (D) preprocessing to filter out problematic samples (e.g., noisy inputs).
