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Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation
Authors
Castiglioni P
Chun-Yuan Chang
+15 more
Cleophas TJM
Greenhalgh T
Gupta J
Jiann-Shing Shieh
Li Tseng
Lu CW
Maysam F. Abbod
Reinhard M
Reinhard M
Sornmo L
Sung-Chun Tang
Tang S C
Thayer J F
Thayer J F
Yi-Ching Lin
Publication date
6 September 2013
Publisher
'Bentham Science Publishers Ltd.'
Doi
View
on
PubMed
Abstract
Copyright © Tseng et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.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.The Stroke Center and Department of Neurology, National Taiwan University, National Science Council in Taiwan, and the Center for Dynamical Biomarkers and Translational Medicine, National Central University, which is sponsored by National Science Council and Min-Sheng General Hospital Taoyuan
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info:doi/10.2174%2F18741207201...
Last time updated on 05/06/2019
Brunel University Research Archive
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oai:bura.brunel.ac.uk:2438/816...
Last time updated on 01/05/2014