Suppr超能文献

经验模态分解用于评估人体运动期间自发变异性的压力反射增益。

Empirical mode decomposition to assess baroreflex gain from spontaneous variability during exercise in humans.

作者信息

Magagnin V, Bassani T, Lucini D, Pagani M, Caiani E G, Cerutti S, Porta A

机构信息

Orthopaedic Institute IRCCS Galeazzi, Milano, Italy.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2236-9. doi: 10.1109/IEMBS.2009.5335008.

Abstract

Estimation of the baroreflex gain has become an important tool in clinical practice in order to assess cardiac autonomic system control. Spectral analysis and sequence analysis techniques based on the spontaneous variability of systolic arterial pressure and heart period have been proposed to evaluate the baroreflex gain. These analyses can be significantly altered by the presence of nonstationarities. Recently, the empirical mode decomposition (EMD), a signal processing technique particularly suitable for nonstationary series, has been proposed as a new tool for data analysis. The aim of this study is to propose EMD-based approaches to the evaluation of the baroreflex gain to account for the possible presence of nonstationarities of systolic arterial pressure and heart period series.

摘要

为了评估心脏自主神经系统的控制情况,压力反射增益的估计已成为临床实践中的一项重要工具。基于收缩期动脉压和心动周期的自发变异性的频谱分析和序列分析技术已被提出用于评估压力反射增益。这些分析可能会因非平稳性的存在而发生显著改变。最近,经验模态分解(EMD)作为一种特别适用于非平稳序列的信号处理技术,已被提出作为数据分析的一种新工具。本研究的目的是提出基于EMD的方法来评估压力反射增益,以应对收缩期动脉压和心动周期序列可能存在的非平稳性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验