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Open AccessArticle

Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis

STMicroelectronics, ADG Group—Central R&D, 95121 Catania, Italy
Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
Author to whom correspondence should be addressed.
Computation 2018, 6(3), 46;
Received: 30 July 2018 / Revised: 20 August 2018 / Accepted: 23 August 2018 / Published: 28 August 2018
(This article belongs to the Section Computational Biology)
Blood Pressure (BP) is one of the most important physiological indicators that provides useful information in the field of health-care monitoring. Blood pressure may be measured by both invasive and non-invasive methods. A novel algorithmic approach is presented to estimate systolic and diastolic blood pressure accurately in a way that does not require any explicit user calibration, i.e., it is non-invasive and cuff-less. The approach herein described can be applied in a medical device, as well as in commercial mobile smartphones by an ad hoc developed software based on the proposed algorithm. The authors propose a system suitable for blood pressure estimation based on the PhotoPlethysmoGraphy (PPG) physiological signal sampling time-series. Photoplethysmography is a simple optical technique that can be used to detect blood volume changes in the microvascular bed of tissue. It is non-invasive since it takes measurements at the skin surface. In this paper, the authors present an easy and smart method to measure BP through careful neural and mathematical analysis of the PPG signals. The PPG data are processed with an ad hoc bio-inspired mathematical model that estimates systolic and diastolic pressure values through an innovative analysis of the collected physiological data. We compared our results with those measured using a classical cuff-based blood pressure measuring device with encouraging results of about 97% accuracy. View Full-Text
Keywords: PPG; blood pressure; neural network PPG; blood pressure; neural network
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MDPI and ACS Style

Rundo, F.; Ortis, A.; Battiato, S.; Conoci, S. Advanced Bio-Inspired System for Noninvasive Cuff-Less Blood Pressure Estimation from Physiological Signal Analysis. Computation 2018, 6, 46.

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