  Photoplethysmographic (PPG) signals offer diagnostic potential beyond heart
rate analysis or blood oxygen level monitoring. In the recent past, research
focused extensively on non-invasive PPG-based approaches to blood pressure (BP)
estimation. These approaches can be subdivided into regression and
classification methods. The latter assign PPG signals to predefined BP
intervals that represent clinically relevant ranges. The former predict
systolic (SBP) and diastolic (DBP) BP as continuous variables and are of
particular interest to the research community. However, the reported accuracies
of BP regression methods vary widely among publications with some authors even
questioning the feasibility of PPG-based BP regression altogether. In our work,
we compare BP regression and classification approaches. We argue that BP
classification might provide diagnostic value that is equivalent to regression
in many clinically relevant scenarios while being similar or even superior in
terms of performance. We compare several established neural architectures using
publicly available PPG data for SBP regression and classification with and
without personalization using subject-specific data. We found that
classification and regression models perform similar before personalization.
However, after personalization, the accuracy of classification based methods
outperformed regression approaches. We conclude that BP classification might be
preferable over BP regression in certain scenarios where a coarser segmentation
of the BP range is sufficient.
