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

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

Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients

This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.This work has received research funding from the Spanish government (www.micinn.es) under project TEC2012 34306 (DiagnoSIS, Diagnosis by means of Statistical Intelligent Systems, 70K€) and projects P09-TIC-4530 (300K€) and P11-TIC-7103 (156K€) from the Andalusian government (http://www.juntadeandalucia.es/organismo​s/economiainnovacioncienciayempleo.html)

Statistical modeling of the atrioventricular node during atrial fibrillation: data length and estimator performance

The atrioventricular (AV) node plays a central role during atrial fibrillation (AF). We have recently proposed a statistical AV node model defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing either one of the dual AV nodal pathways, the refractory periods of the pathways, and the prolongation of refractory periods. All model parameters are estimated from the RR series using maximum likelihood (ML) estimation, except for the mean arrival rate of atrial impulses which is estimated by the AF frequency derived from the f-waves. The aim of this study is to present a unified approach to ML estimation which also involves the shorter refractory period, thus avoiding our previous Poincaré plot analysis which becomes biased. In addition, the number of RR intervals required for accurate parameter estimation is presented. The results show that the shorter refractory period can be accurately estimated, and that the resulting estimates converge to the true values when about 500 RR intervals are available

Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation

Statistical modeling of atrioventricular (AV) nodal function during atrial fibrillation (AF) is revisited for the purpose of defining model properties and improving parameter estimation. The characterization of AV nodal pathways is made more detailed and the number of pathways is now determined by the Bayesian information criterion, rather than just producing a probability as was previously done. Robust estimation of the shorter refractory period (i.e., of the slow pathway) is accomplished by a Hough-based technique which is applied to a Poincare plot of RR intervals. The performance is evaluated on simulated data as well as on ECG data acquired from AF patients during rest and head-up tilt test. The simulation results suggest that the refractory period of the slow pathway can be accurately estimated even in the presence of many artifacts. They also show that the number of pathways can be accurately determined. The results from ECG data show that the refined AV node model provides significantly better fit than did the original model, increasing from 85 +/- 5% to 88 +/- 4% during rest, and from 86 +/- 5% to 87 +/- 3% during tilt. When assessing the effect of sympathetic stimulation, the AF frequency increased significantly during tilt (6.25 +/- 0.58 Hz vs. 6.32 +/- 0.61 Hz, p <0.05, rest vs. tilt) and the prolongation of the refractory periods of both pathways decreased significantly (slow pathway: 0.23 +/- 0.20 s vs. 0.11 +/- 0.10 s, p <0.001, rest vs. tilt; fast pathway: 0.24 +/- 0.31 s vs. 0.16 +/- 0.19s, p <0.05, rest vs. tilt). The results show that AV node characteristics can be assessed noninvasively for the purpose of quantifying changes induced by autonomic stimulation. (C) 2012 Elsevier Ltd. All rights reserved