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At least 829 records · Page 46Linked to original sources
PubMed2004

Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes.

This paper introduces a modified principal dynamic modes (PDM) method, which is able to separate the dynamics of sympathetic and parasympathetic nervous activities. The PDM is based on the principle that among all possible choices of expansion bases, there are some that require the minimum number of basis functions to achieve a given mean-square approximation of the system output. Such a minimum set of basis functions is termed PDMs of the nonlinear system. We found that the first two dominant PDMs have similar frequency characteristics for parasympathetic and sympathetic activities, as reported in the literature. These results are consistent for all nine of our healthy human subjects using our modified PDM approach. Validation of the purported separation of parasympathetic and sympathetic activities was performed by the application of the autonomic nervous system blocking drugs atropine and propranolol. With separate applications of the respective drugs, we found a significant decrease in the amplitude of the waveforms that correspond to each nervous activity. Furthermore, we observed near complete elimination of these dynamics when both drugs were given to the subjects. Comparison of our method to the conventional low-frequency/high-frequency ratio shows that our proposed approach provides more accurate assessment of the autonomic nervous balance. Our nonlinear PDM approach allows a clear separation of the two autonomic nervous activities, the lack of which has been the main reason why heart rate variability analysis has not had wide clinical acceptance.

Adult↗
PubMed2004

Solving the inverse problem of electrocardiography using a Duncan and Horn formulation of the Kalman filter.

Numeric regularization methods most often used to solve the ill-posed inverse problem of electrocardiography are spatial and ignore the temporal nature of the problem. In this paper, a Kalman filter reformulation incorporated temporal information to regularize the inverse problem, and was applied to reconstruct left ventricular endocardial electrograms based on cavitary electrograms measured by a noncontact, multielectrode probe. These results were validated against in situ electrograms measured with an integrated, multielectrode basket-catheter. A three-dimensional, probe-endocardium model was determined from multiplane fluoroscopic images. The boundary element method was applied to solve the boundary value problem and determine a linear relationship between endocardial and probe potentials. The Duncan and Horn formulation of the Kalman filter was employed and was compared to the commonly used zero- and first-order Tikhonov spatial regularization as well as the Twomey temporal regularization method. Endocardial electrograms were reconstructed during both sinus and paced rhythms. The Paige and Saunders solution of the Duncan and Horn formulation reconstructed endocardial electrograms at an amplitude relative error of 13% (potential amplitude) which was superior to solutions obtained with zero-order Tikhonov (relative error, 31%), first-order Tikhonov (relative error, 19%), and Twomey regularization (relative error, 44%). Likewise, activation time error in the inverse solution using the Duncan and Horn formulation (2.9 ms) was smaller than that of zero-order Tikhonov (4.8 ms), first-order Tikhonov (5.4 ms), and Twomey regularization (5.8 ms). Therefore, temporal regularization based on the Duncan and Horn formulation of the Kalman filter improves the solution of the inverse problem of electrocardiography.

Algorithms↗
PubMed2004

Proposed corrections for the quantification of coupling patterns by recurrence plots.

The aim of this paper is to quantify the coupling during phase-locking patterns by using the recurrence plot quantification approach. We found that the percent determinism of the recurrences and the entropy of recurrences--corrected for border effect induced by finite data lenght--succeeded in distinguishing three coupling conditions both in simulation signals and in real data from cardiorespiratory synchronization experiments.

Biological Clocks↗
PubMed2004

Computationally effective algorithm for robust weighted averaging.

One of the greatest disadvantages of the weighted signal averaging method is its sensitivity to the presence of noise and outliers in data and the need to estimate the noise variance in all signal cycles. The robust weighted averaging method based on the epsilon-insensitive loss function is free of these disadvantages, but has a very high computational burden and requires a choice of the insensitivity parameter epsilon. In this study, a new computationally effective algorithm for robust weighted averaging with automatic adjustment of the insensitivity parameter is introduced.

Algorithms↗
PubMed2004

A comparison of noninvasive reconstruction of epicardial versus transmembrane potentials in consideration of the null space.

We compare two source formulations for the electrocardiographic forward problem in consideration of their implications for regularizing the ill-posed inverse problem. The established epicardial potential source model is compared with a bidomain-theory-based transmembrane potential source formulation. The epicardial source approach is extended to the whole heart surface including the endocardial surfaces. We introduce the concept of the numerical null and signal space to draw attention to the problems associated with the nonuniqueness of the inverse solution and show that reconstruction of null-space components is an important issue for physiologically meaningful inverse solutions. Both formulations were tested with simulated data generated with an anisotropic heart model and with clinically measured data of two patients. A linear and a recently proposed quasi-linear inverse algorithm were applied for reconstructions of the epicardial and transmembrane potential, respectively. A direct comparison of both formulations was performed in terms of computed activation times. We found the transmembrane potential-based formulation is a more promising source formulation as stronger regularization by incorporation of biophysical a priori information is permitted.

Adult↗
PubMed2004

A theoretical model of the high-frequency arrhythmogenic depolarization signal following myocardial infarction.

Theoretical body-surface potentials were computed from single, branching and tortuous strands of Luo-Rudy dynamic model cells, representing different areas of an infarct scar. When action potential (AP) propagation either in longitudinal or transverse direction was slow (3-12 cm/s), the depolarization signals contained high-frequency (100-300 Hz) oscillations. The frequencies were related to macroscopic propagation velocity and strand architecture by simple formulas. Next, we extended a mathematical model of the QRS-complex presented in our earlier work to simulate unstable activation wavefront. It combines signals from different strands with small timing fluctuations relative to a large repetitive QRS-like waveform and can account for dynamic changes of real arrhythmogenic micropotentials. Variance spectrum of wavelet coefficients calculated from the composite QRS-complex contained the high frequencies of the individual abnormal signals. We conclude that slow AP propagation through fibrotic regions after myocardial infarction is a source of high-frequency arrhythmogenic components that increase beat-to-beat variability of the QRS, and wavelet variance parameters can be used for ventricular tachycardia risk assessment.

Arrhythmias, Cardiac↗
PubMed2005

Measuring curvature and velocity vector fields for waves of cardiac excitation in 2-D media.

Excitable media theory predicts the effect of electrical wavefront morphology on the dynamics of propagation in cardiac tissue. It specifies that a convex wavefront propagates slower and a concave wavefront propagates faster than a planar wavefront. Because of this, wavefront curvature is thought to be an important functional mechanism of cardiac arrhythmias. However, the curvature of wavefronts during an arrhythmia are generally unknown. We introduce a robust, automated method to measure the curvature vector field of discretely characterized, arbitrarily shaped, two-dimensional (2-D) wavefronts. The method relies on generating a smooth, continuous parameterization of the shape of a wave using cubic smoothing splines fitted to an isopotential at a specified level, which we choose to be -30 mV. Twice differentiating the parametric form provides local curvature vectors along the wavefront and waveback. Local conduction velocities are computed as the wave speed along lines normal to the parametric form. In this way, the curvature and velocity vector field for wavefronts and wavebacks can be measured. We applied the method to data sampled from a 2-D numerical model and several examples are provided to illustrate its usefulness for studying the dynamics of cardiac propagation in 2-D media.

Algorithms↗
PubMed2005

Multilayer modeling of reflectance pulse oximetry.

A multilayer tissue description was employed in Monte Carlo simulations of reflectance pulse oximetry to study the impact of assumptions made in previous studies employing homogeneous tissue models. Simulation results with a discrete layer of arterial pulsatility were similar to previous studies employing homogenous tissue models. However, the relationship of normalized pulse amplitude to emitter-detector spacing reiterates that spacing has a significant impact on pulse oximetry function. The effect of melanin content as a thin, superficial absorber was also simulated, with results supporting the general clinical observation that skin shade need not substantially compromise pulse oximeter accuracy.

Algorithms↗
PubMed2005

Fetal electrocardiogram extraction by sequential source separation in the wavelet domain.

This paper addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub- and super-Gaussian signals is mitigated, as shown by experimental results.

Algorithms↗
PubMed2005

A model order selection criterion with applications to cardio-respiratory-renal systems.

We introduce a model order selection criterion called signal prediction error (SPE) for the identification of a linear regression model, which can be an adequate representation of a resting physiologic system. SPE is an estimate of the prediction error variance due only to model estimation error and not unobserved noise, which distinguishes it from the widely used final prediction error (FPE). We then present a theoretical analysis of SPE, which predicts that its ability to select correctly the model order is more dependent on the signal-to-noise ratio (SNR) and less dependent on the number of data samples available for analysis. We next propose a heuristic procedure based on SPE (called SPE(D)) to improve its robustness to SNR levels. We then demonstrate, through simulated physiologic data at high SNR levels, that SPE will be equivalent to consistent model order selection criteria for long data records but will become superior to FPE and other model order selection criteria as the size of the data record decreases. The simulated data results also show that SPE(D) is indeed a significant improvement over SPE in terms of robustness to SNR. Finally, we demonstrate the applicability of SPE and SPE(D) to actual cardio-respiratory-renal data.

Algorithms↗
PubMed2005

Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales.

The autonomic information flow (AIF) represents the complex communication within the Autonomic Nervous System (ANS). It can be assessed by the mutual information function (MIF) of heart rate fluctuations (HRF). The complexity of HRF is based on several interacting physiological mechanisms operating at different time scales. Therefore one prominent time scale for HRF complexity analysis is not given a priori. The MIF reflects the information flow at different time scales. This approach is defined and evaluated in the present paper. In order to aggregate relevant physiological time scales, the MIF of HRF obtained from eight adult Lewis rats during the awake state, under general anesthesia, with additional vagotomy, and additional betal-adrenergic blockade are investigated. Physiologically relevant measures of the MIF were assessed with regard to the discrimination of these states. A simulation study of a periodically excited pendulum is performed to clarify the influence of the time scale of MIF in comparison to the Kolmogorov Sinai entropy (KSE) of that well defined system. The general relevance of the presented AIF approach was confirmed by comparing mutual information, approximate entropy, and sample entropy at their respective time scales.

Algorithms↗
PubMed2005

Quantifying errors in spectral estimates of HRV due to beat replacement and resampling.

Spectral estimates of heart rate variability (HRV) often involve the use of techniques such as the fast Fourier transform (FFT), which require an evenly sampled time series. HRV is calculated from the variations in the beat-to-beat (RR) interval timing of the cardiac cycle which are inherently irregularly spaced in time. In order to produce an evenly sampled time series prior to FFT-based spectral estimation, linear or cubic spline resampling is usually employed. In this paper, by using a realistic artificial RR interval generator, interpolation and resampling is shown to result in consistent over-estimations of the power spectral density (PSD) compared with the theoretical solution. The Lomb-Scargle (LS) periodogram, a more appropriate spectral estimation technique for unevenly sampled time series that uses only the original data, is shown to provide a superior PSD estimate. Ectopy removal or replacement is shown to be essential regardless of the spectral estimation technique. Resampling and phantom beat replacement is shown to decrease the accuracy of PSD estimation, even at low levels of ectopy or artefact. A linear relationship between the frequency of ectopy/artefact and the error (mean and variance) of the PSD estimate is demonstrated. Comparisons of PSD estimation techniques performed on real RR interval data during minimally active segments (sleep) demonstrate that the LS periodogram provides a less noisy spectral estimate of HRV.

Adult↗
PubMed2005

Simulation of ST segment changes during subendocardial ischemia using a realistic 3-D cardiac geometry.

The mechanisms underlying the ST segment shifts associated with subendocardial ischemia remain unclear. The aim of this paper is to shed further light on the subject through numerical simulations of these shifts. A realistic three-dimensional model of the ventricles, including fiber rotation and anisotropy, is embedded in a nonhomogeneous torso model. A simplification of the bidomain model is used to calculate only the ST segment shift, assuming known values of the transmembrane potential during the plateau and rest phases. A similar simulation is performed in two dimensions. The simulation results suggest that subendocardial ischemia can be located by ST segment shift on the epicardial and torso surfaces. It is shown that ST elevation is associated with the transmural ischemic boundary, while ST depression is associated with the lateral ischemic boundaries.

Body Surface Potential Mapping↗
PubMed2005

Orthonormal-basis partitioning and time-frequency representation of cardiac rhythm dynamics.

Although a number of time-frequency representations have been proposed for the estimation of time-dependent spectra, the time-frequency analysis of multicomponent physiological signals, such as beat-to-beat variations of cardiac rhythm or heart rate variability (HRV), is difficult. We thus propose a simple method for 1) detecting both abrupt and slow changes in the structure of the HRV signal, 2) segmenting the nonstationary signal into the less nonstationary portions, and 3) exposing characteristic patterns of the changes in the time-frequency plane. The method, referred to as orthonormal-basis partitioning and time-frequency representation (OPTR), is validated using simulated signals and actual HRV data. Here we show that OPTR can be applied to long multicomponent ambulatory signals to obtain the signal representation along with its time-varying spectrum.

Algorithms↗
PubMed2005

Estimation of coherence between blood flow and spontaneous EEG activity in neonates.

Blood flow to the brain responds to changes in neuronal activity and, thus, metabolic demand. In earlier work, we observed correlation between cerebral blood flow and spontaneous electroencephalogram (EEG) activity in neonates. Using coherence, we now found that during Tracé Alternant EEG activity in quiet sleep of normal term neonates, this correlation is strongest at frequencies around 0.1 Hz, reaching statistical significance (p < 0.05) in six of the nine subjects studied (p < 0.07 in eight subjects). Due to noise, artifact, and spontaneous changes in the subjects' EEG patterns, the signals investigated included epochs of missing samples. We, therefore, developed a novel algorithm for the estimation of coherence in such data and applied a Monte Carlo (surrogate data) method for its statistical analysis. This process provides a test for the statistical significance of the maximum coherence within a selected frequency band. In addition to permitting further insight into the mechanisms of cerebral blood flow control, these algorithms are potentially of great benefit in a wide range of biomedical applications, where interrupted (gapped) recordings are often a problem.

Algorithms↗
PubMed2005

Bayesian solutions and performance analysis in bioelectric inverse problems.

In bioelectric inverse problems, one seeks to recover bioelectric sources from remote measurements using a mathematical model that relates the sources to the measurements. Due to attenuation and spatial smoothing in the medium between the sources and the measurements, bioelectric inverse problems are generally ill-posed. Bayesian methodology has received increasing attention recently to combat this ill-posedness, since it offers a general formulation of regularization constraints and additionally provides statistical performance analysis tools. These tools include the estimation error covariance and the marginal probability density of the measurements (known as the "evidence") that allow one to predictively quantify and compare experimental designs. These performance analysis tools have been previously applied in inverse electroencephalography and magnetoencephalography, but only in relatively simple scenarios. The main motivation here was to extend the utility of Bayesian estimation techniques and performance analysis tools in bioelectric inverse problems, with a particular focus on electrocardiography. In a simulation study we first investigated whether Bayesian error covariance, computed without knowledge of the true sources and based on instead statistical assumptions, accurately predicted the actual reconstruction error. Our study showed that error variance was a reasonably reliable qualitative and quantitative predictor of estimation performance even when there was error in the prior model. We also examined whether the evidence statistic accurately predicted relative estimation performance when distinct priors were used. In a simple scenario our results support the hypothesis that the prior model that maximizes the evidence is a good choice for inverse reconstructions.

Algorithms↗
PubMed2005

Finite volume stiffness matrix for solving anisotropic cardiac propagation in 2-D and 3-D unstructured meshes.

The finite volume method (FVM) has been shown recently to be an effective method for discretizing the reaction-diffusion equations that govern wavefront propagation in anisotropic cardiac tissue, as it can naturally handle both complex geometries and no flux boundary conditions without the use of ghost nodes. This communication presents an alternative formulation of FVM for triangle and tetrahedral meshes using the concept of dual basis. An algorithm based on this form is given that leads to an efficient computation of the stiffness matrix, facilitating the incorporation of space adaptive schemes and time varying material properties into numerical simulations of cardiac dynamics.

Action Potentials↗