非线性和常规生物信号分析应用于倾斜试验评价自主神经系统和自我调节。

Q3 Medicine Open Biomedical Engineering Journal Pub Date : 2013-09-06 eCollection Date: 2013-01-01 DOI:10.2174/1874120720130905004
Li Tseng, Sung-Chun Tang, Chun-Yuan Chang, Yi-Ching Lin, Maysam F Abbod, Jiann-Shing Shieh
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引用次数: 11

摘要

倾斜试验(TTT)是怀疑自主神经系统(ANS)功能障碍或不明原因晕厥患者的标准检查。目前,基于TTT期间血压(BP)或心率(HR)变化的分析方法是线性的,但正常的BP和HR生理调节被认为主要是非线性的。因此,本研究分为两部分:第一部分是分析TTT期间的HR,并与三种方法进行比较,以区分正常对照和ANS功能障碍受试者。第一种方法是功率谱密度(PSD),第二种方法是去趋势波动分析(DFA),第三种方法是多尺度熵(MSE)来计算系统复杂性。研究的第二部分是分析TTT期间血压和脑血流速度(CBFV)的变化。采用相关系数分析(nMxa)和MSE两种方法对结果进行比较。本研究第一部分得出PSD的低频功率与总功率之比,以及MSE方法比DFA方法更能区分正常对照与患者组之间的差异。而在第二部分中,三个阶段移动平均窗口的nMxa优于三个阶段一起使用的nMxa。此外,MSE对BP数据的分析效果优于CBFV数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation.

Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.

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来源期刊
Open Biomedical Engineering Journal
Open Biomedical Engineering Journal Medicine-Medicine (miscellaneous)
CiteScore
1.60
自引率
0.00%
发文量
4
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