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.
Advanced multi-neural system for cuff-less blood pressure estimation through nonlinear HC-features
Conoci S.
2019-01-01
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.Pubblicazioni consigliate
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