Advanced Multi-neural System for Cuff-less Blood Pressure Estimation through Nonlinear HC-features
Advanced Multi-neural System for Cuff-less Blood Pressure Estimation through Nonlinear HC-features
F. Rundo,A. Ortis,S. Battiato,S. Conoci
TLDR
A novel algorithmic approach to accurately estimate both systolic and diastolic blood pressure is presented that exploits the PhotoPlethysmoGraphy (PPG) signal pattern acquired by non-invasive and cuff-less Physio-Probe (PP) silicon-based SiPM device.
Abstract
Blood Pressure (BP) is one of the most important physiological indicator that can provide useful information in the medical field. BP is usually measured by a sphygmomanometer device, which is composed by a cuff and a mechanical manometer. In this paper, a novel algorithmic approach to accurately estimate both systolic and diastolic blood pressure is presented. This algorithm exploits the PhotoPlethysmoGraphy (PPG) signal pattern acquired by non-invasive and cuff-less Physio-Probe (PP) silicon-based SiPM device. The PPG data are then processed with ad-hoc bio-inspired mathematical model which estimates both systolic and diastolic pressure values. We compared our results with those measured using a classical sphygmomanometer device and encouraging results of about 97% accuracy were achieved.