Applicability of the single equivalent point dipole model to represent a spatially distributed bio-electrical source.

Although the single equivalent point dipole model has been used to represent well-localised bio-electrical sources, in realistic situations the source is distributed. Consequently, position estimates of point dipoles determined by inverse algorithms suffer from systematic error due to the non-exact applicability of the inverse model. In realistic situations, this systematic error cannot be avoided, a limitation that is independent of the complexity of the torso model used. This study quantitatively investigates the intrinsic limitations in the assignment of a location to the equivalent dipole due to distributed electrical source. To simulate arrhythmic activity in the heart, a model of a wave of depolarisation spreading from a focal source over the surface of a spherical shell is used. The activity is represented by a sequence of concentric belt sources (obtained by slicing the shell with a sequence of parallel plane pairs), with constant dipole moment per unit length (circumferentially) directed parallel to the propagation direction. The distributed source is represented by N dipoles at equal arc lengths along the belt. The sum of the dipole potentials is calculated at predefined electrode locations. The inverse problem involves finding a single equivalent point dipole that best reproduces the electrode potentials due to the distributed source. The inverse problem is implemented by minimising the chi2 per degree of freedom. It is found that the trajectory traced by the equivalent dipole is sensitive to the location of the spherical shell relative to the fixed electrodes. It is shown that this trajectory does not coincide with the sequence of geometrical centres of the consecutive belt sources. For distributed sources within a bounded spherical medium, displaced from the sphere's centre by 40% of the sphere's radius, it is found that the error in the equivalent dipole location varies from 3 to 20% for sources with size between 5 and 50% of the sphere's radius. Finally, a method is devised to obtain the size of the distributed source during the cardiac cycle.

Electrical and structural remodeling of the failing ventricle.

Heart failure (HF) is a complex disease that presents a major public health challenge to Western society. The prevalence of HF increases with age in the elderly population, and the societal disease burden will increase with prolongation of life expectancy. HF is initially characterized by an adaptive increase of neurohumoral activation to compensate for reduction of cardiac output. This leads to a combination of neurohumoral activation and mechanical stress in the failing heart that trigger a cascade of maladaptive electrical and structural events that impair both the systolic and diastolic function of the heart.

Accuracy of a single equivalent moving dipole model in a realistic anatomic geometry torso model.

In this study, we investigated the accuracy of an algorithm to identify the spatial single equivalent moving dipole parameters in a realistic anatomic geometry torso model from potentials at the body surface. Specifically we investigated the effect of measurement noise, and dipole position and orientation in the accuracy of the algorithm. The boundary element method was used to calculate the forward potential distribution at 64 electrode positions on the body surface due to a point dipole. The mean and standard deviation of the distance of the true (obtained in the forward potential calculation) minus the estimated dipole location (obtained from the inverse algorithm) was estimated for each of the above three cases. Our results indicate that the dipole position has the most significant influence on the accuracy of our inverse algorithm.

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks.

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals.

T-wave alternans and dispersion of the QT interval as risk stratification markers in patients susceptible to sustained ventricular arrhythmias.

T-wave alternans and QT dispersion were compared as predictors of the outcome of electrophysiologic study and arrhythmia-free survival in patients undergoing electrophysiologic evaluation. T-wave alternans was a highly significant predictor of these 2 outcome variables, whereas QT dispersion was not.

Prognostic significance of electrical alternans versus signal averaged electrocardiography in predicting the outcome of electrophysiological testing and arrhythmia-free survival.

OBJECTIVE: To investigate the accuracy of signal averaged electrocardiography (SAECG) and measurement of microvolt level T wave alternans as predictors of susceptibility to ventricular arrhythmias. DESIGN: Analysis of new data from a previously published prospective investigation. SETTING: Electrophysiology laboratory of a major referral hospital. PATIENTS AND INTERVENTIONS: 43 patients, not on class I or class III antiarrhythmic drug treatment, undergoing invasive electrophysiological testing had SAECG and T wave alternans measurements. The SAECG was considered positive in the presence of one (SAECG-I) or two (SAECG-II) of three standard criteria. T wave alternans was considered positive if the alternans ratio exceeded 3.0. MAIN OUTCOME MEASURES: Inducibility of sustained ventricular tachycardia or fibrillation during electrophysiological testing, and 20 month arrhythmia-free survival. RESULTS: The accuracy of T wave alternans in predicting the outcome of electrophysiological testing was 84% (p < 0.0001). Neither SAECG-I (accuracy 60%; p < 0.29) nor SAECG-II (accuracy 71%; p < 0.10) was a statistically significant predictor of electrophysiological testing. SAECG, T wave alternans, electrophysiological testing, and follow up data were available in 36 patients while not on class I or III antiarrhythmic agents. The accuracy of T wave alternans in predicting the outcome of arrhythmia-free survival was 86% (p < 0.030). Neither SAECG-I (accuracy 65%; p < 0.21) nor SAECG-II (accuracy 71%; p < 0.48) was a statistically significant predictor of arrhythmia-free survival. CONCLUSIONS: T wave alternans was a highly significant predictor of the outcome of electrophysiological testing and arrhythmia-free survival, while SAECG was not a statistically significant predictor. Although these results need to be confirmed in prospective clinical studies, they suggest that T wave alternans may serve as a non-invasive probe for screening high risk populations for malignant ventricular arrhythmias.

Clinical utility of T-wave alternans.

Electrical alternans represents a variation in the morphology of electrocardiographic complexes on an every-other-beat basis in an ABABAB... pattern. Apparent electrical alternans associated with pericardial effusion results from rotation of the heart in the pericardial sac, and not true alternation in electrical conduction patterns. In contrast, true electrical alternans results from an alternation in electrical conduction patterns in the heart itself. Repolarization alternans is true electrical alternans associated with the ST segment and T wave of the electrocardiogram (ECG). Here we will focus on T-wave alternans (TWA) and its association with susceptibility to ventricular tachyarrhythmias. Electrical alternans was reported in the literature as early as 1909. Historically, electrical alternans has been regarded as a fairly rare electrocardiographic abnormality. Case reports of electrical alternans have been associated with a variety of disease states, including acute ischemia, Prinzmetal's angina, a variety of electrolyte abnormalities, and the long QT syndrome. Interestingly, patients born with the prolonged QT syndrome have a very high incidence of sudden cardiac death at an early age. Schwartz and Malliani showed that patients with the prolonged QT syndrome who do not demonstrate alternans at rest, may evidence alternans during stress such as emotional excitement. Thus, over the years electrical alternans has been associated anecdotally with conditions associated with an increased risk of ventricular arrhythmias. In 1948, Kalter reviewed the world literature on electrical alternans and found a total of 41 reported cases. In addition, he reviewed clinical ectrocardiograms from 6059 patients and found five new cases (incidence of less than 1 in 1000 patients). Interestingly, he found a very high mortality, 62%, associated with this condition. Despite the clinical associations reported in the literature, the consensus view of electrical alternans until recent years has been that alternans is an electrocardiographic curiosity rarely encountered in clinical practice which, when identified, does not have specific clinical significance.