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Photoplethysmographic (PPG) signals offer diagnostic potential beyond heartrate analysis or blood oxygen level monitoring. In the recent past, researchfocused extensively on non-invasive PPG-based approaches to blood pressure (BP)estimation. These approaches can be subdivided into regression andclassification methods. The latter assign PPG signals to predefined BPintervals that represent clinically relevant ranges. The former predictsystolic (SBP) and diastolic (DBP) BP as continuous variables and are ofparticular interest to the research community. However, the reported accuraciesof BP regression methods vary widely among publications with some authors evenquestioning the feasibility of PPG-based BP regression altogether. In our work,we compare BP regression and classification approaches. We argue that BPclassification might provide diagnostic value that is equivalent to regressionin many clinically relevant scenarios while being similar or even superior interms of performance. We compare several established neural architectures usingpublicly available PPG data for SBP regression and classification with andwithout personalization using subject-specific data. We found thatclassification and regression models perform similar before personalization.However, after personalization, the accuracy of classification based methodsoutperformed regression approaches. We conclude that BP classification might bepreferable over BP regression in certain scenarios where a coarser segmentationof the BP range is sufficient.

Regression or Classification? Reflection on BP prediction from PPG data using Deep Neural Networks in the scope of practical applications