A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.

ABSTRACT: Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shi

SUBMITTER: Gonzalez S 

PROVIDER: S-EPMC10030661 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature