A review of machine learning methods for non-invasive blood pressure estimation.

Abstract

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension, both of which increasing morbidity for a wide variety of reasons. This monitoring can be done either invasively or non-invasively and intermittently vs. continuously. An invasive method is considered the gold standard and provides continuous measurement, but it carries higher risks of complications such as infection, bleeding, and thrombosis. Non-invasive techniques, in contrast, reduce these risks and can provide intermittent or continuous blood pressure readings. This review explores modern machine learning-based non-invasive methods for blood pressure estimation, discussing their advantages, limitations, and clinical relevance.