A Novel Hybrid Framework for Cuff-Less Blood Pressure Estimation based On Vital Bio Signals processing using Machine Learning

Abstract

Blood Pressure is one among the most important physiological parameters for assessing the overall well being of an individual. It plays pivotal role in the detection of many cardiovascular diseases specially Hypertension. Traditional Cuff-Based BP measurements techniques have several drawbacks and they are significantly inconvenient to patients, moreover continuous BP measurement is difficult. Lot of research is currently going on for Cuff-Less BP Estimation and several techniques are researched out in the researcher’s community. However, most of the existing approaches lack the required level of accuracy, generality and they are not experimented out on a large population of having heterogeneous subjects with varied demographic features. In this paper we propose a novel hybrid signal processing approach using machine learning for continuous estimation of BP without the need for calibration. Our proposed framework has reached satisfactory results in terms of Mean Absolute Error (MAE) for mean arterial pressure (MAP) Estimation.