Current solutions for blood pressure monitoring can be classified as invasive or non-invasive,
both with drawbacks. Invasive blood pressure monitoring can lead to complications. Non-invasive
blood pressure monitoring is intermittent which leads to missed episodes of hypertension and hy-
potension, also leading to complications. The state of the art for blood pressure monitoring through
machine learning methods usually requires personalization, which is prohibitive in a clinical appli-
cation. These proposed methods are generally not evaluated for clinical application. Datasets are
usually split randomly, while a patient-wise split is required.
We first start by performing a survey of the literature to find candidate models for evaluation.
These models are reproduced for evaluation alongside our proposed models. Popular input modal-
ities from the literature are also reproduced with our proposed input modality. All combinations of
models and input modalities are then evaluated against a patient-wise and random split. We perform
a learning curve analysis to estimate how much data would be required to pass the AAMI standard.
The performance results establish that no model can provide calibration-free, non-invasive blood
pressure monitoring using a single PPG site. The performance metrics show that our models and
input modalities outperform the state of the art for random and patient-wise splits. Comparison
against the models demonstrates that model complexity is insufficient to achieve better performance
and that better preprocessing is a more efficient way to improve performance. The learning-curve
analysis estimates that additional data could help achieve a model that passes the AAMI standard.