Artificial Intelligence Model for an Electrocardiography-based Blood Pressure Estimation System

Abstract

In this study, we propose a novel artificial intelligence model for blood pressure estimation that establishes a method to estimate both systolic and diastolic blood pressures based on an electrocardiogram. Experimental results show that the root mean square errors for systolic and diastolic blood pressures are 3.82 and 2.17, respectively. Therefore, the proposed approach complies with the Association for the Advancement of Medical Instrumentation standard. The proposed structure is feasible and can be implemented by being integrated with electrode sensors and a signal processing platform. In the future, this technology can replace home care systems or wearable devices to provide warnings of health issues.