An Explainable and Formal Framework for Hypertension Monitoring Using ECG and PPG

Abstract

An alarming increase in hypertension is a hazard to global health that poses severe implications for the body’s vital organs. To prevent serious repercussions, hypertension should be monitored continuously for early detection. It is well known that physiological signals, such as the photoplethysmogram (PPG) and electrocardiogram (ECG), carry essential information about the vitals of the human body. Considering this, numerous machine learning-based models based on ECG-PPG have been proposed for monitoring hypertension; however, such models are “non-explainable” and lack clinical interpretation. This work proposes a formal method-based runtime verification approach for hypertension monitoring using ECG and PPG sensing, which is explainable. The pulse arrival time (PAT) feature extracted using both signals is employed to implement a decision tree to infer hypertension patterns/policies defined in PAT, based on which a runtime monitor is synthesized to classify hypertension. Using the MIMIC II dataset, the proposed scheme’s performance is assessed, and the accuracy, sensitivity, and specificity are determined to be 95.7%, 93.9%, and 97.6%, respectively.