心室
心脏病学
校准
联轴节(管道)
计算机科学
工作(物理)
人工神经网络
医学
人工智能
数学
工程类
机械工程
统计
作者
Siegfried Wassertheurer,Christopher Mayer,Felix Breitenecker
标识
DOI:10.1016/j.simpat.2008.04.016
摘要
Abstract The aim of the presented work has been the development of an algorithm for a non-invasive, portable, easy-to-use, and affordable device for measuring systemic cardiovascular parameters such as cardiac output and peripheral resistance. The data acquisition is based on a common oscillometric measurement using an occlusive blood pressure cuff, and no additional calibration is necessary. The novel algorithm introduced here combines several simulation techniques like neural networks or differential equations, which will be explained briefly. The determination of the hemodynamical parameters is based on the idea that the ejection work of the left ventricle is subject to an optimization principle. This kind of model needs no additional external calibration and opens therefore good perspectives for non-expert use in cardiovascular risk stratification and hypertension therapy optimization. To verify the approach we present some clinical results and a relevant discussion on it, followed by a view of future work.
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