Tseng Li, Tang Sung-Chun, Chang Chun-Yuan, Lin Yi-Ching, Abbod Maysam F, Shieh Jiann-Shing
Department of Mechanical Engineering, Yuan Ze University, R.O.C. Taiwan.
Open Biomed Eng J. 2013 Sep 6;7:93-9. doi: 10.2174/1874120720130905004. eCollection 2013.
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.
倾斜试验(TTT)是针对疑似自主神经系统(ANS)功能障碍或不明原因晕厥患者的一项标准检查。目前,基于倾斜试验期间血压(BP)或心率(HR)变化的分析方法是线性的,但BP和HR的正常生理调节被认为主要是非线性的。因此,本研究包括两个部分:第一部分是分析倾斜试验期间的HR,并与三种区分正常对照组和ANS功能障碍受试者的方法进行比较。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)以计算系统的复杂性。研究的第二部分是分析倾斜试验期间BP和脑血流速度(CBFV)的变化。使用两种测量方法来比较结果,即相关系数分析(nMxa)和MSE。本研究的第一部分得出结论,PSD的低频功率与总功率之比以及MSE方法在区分正常对照组和患者组之间的差异方面优于DFA。而在第二部分中,三个阶段移动平均窗口的nMxa优于三个阶段一起的nMxa。此外,使用MSE分析BP数据比分析CBFV数据更好。