A technique for measurement of the extent of spatial organizationof atrial activation during atrial fibrillation in the intact humanheart

Atrial fibrillation (AF) is a common clinical problem, associated with considerable morbidity and motility, for which effective management strategies have yet to be devised. The absence of objective measures to guide selection of antiarrhythmic drug therapy for maintenance of sinus rhythm leaves only clinical endpoints (either beneficial or detrimental) for assessment of drug action, with occasional catastrophic consequences. As part of an attempt to provide an objective framework for the assessment of antiarrhythmic drug action on the electrophysiologic determinants of atrial fibrillation, the authors have developed a measure of the spatial organization of atrial activation processes during atrial fibrillation. By recording activation sequences at multiple equally spaced locations on the endocardial surface of the atrial during atrial fibrillation in humans and determining the degree of correlation between these activation sequences as a function of distance, the authors have been able to construct spatial correlation functions for atrial activation. They have found that atrial activation remains well-correlated, independent of distance during normal sinus rhythm and atrial flutter. During atrial fibrillation, correlation decays monotonically with distance and the space-constant for this decay may be used to describe the relative spatial organization of atrial fibrillation. The authors provide examples of the impact of antiarrhythmic agents on the space-constant and suggest that assessment of the relative spatial organization of atrial activation using this methodology may potentially provide an objective framework to guide therapy in patients with AF     

Computer analysis of the electrocardiogram during esophageal pacingcardiac stress

It has been estimated that 15 to 30% of patients with suspected or known coronary artery disease are unable to perform an adequate exercise stress test due to a variety of reasons such as obesity, poor physical condition, claudication, etc. Transesophageal atrial pacing has been proposed as a noninvasive alternative for inducing cardiac stress in patients who cannot exercise. Although computer analysis is commonly employed to analyze the electrocardiogram (ECG) during the conventional exercise stress test, the surface ECG recorded during transesophageal atrial pacing is contaminated with large pacing artifacts which confound beat identification by standard computer software. We report the development of a robust signal processing algorithm for interpretation of the surface ECG during transesophageal atrial pacing stress. The algorithm employs novel schemes using both linear and nonlinear transformations to detect and differentiate between the pacing artifact and QRS complex even in difficult situations where the pacing artifact is in proximity to or superimposed on the QRS complex. The algorithm uses sophisticated logic for automatic recognition of sustained capture. It subsequently calculates beat-by-beat and average (over five beats) ST segment amplitude and slope. The algorithm also reports the instantaneous heart rate, RR interval, pace-to-R interval, R-wave amplitude, and estimated sinus node recovery time upon loss of sustained capture. The limitations of present exercise ECG computer methods in processing the ECG during transesophageal atrial pacing stress are evaluated and significantly improved performance by our algorithm is demonstrated.     

Identification of cardiac rhythm features by mathematical analysis of vector fields

Automated techniques for locating cardiac arrhythmia features are limited, and cardiologists generally rely on isochronal maps to infer patterns in the cardiac activation sequence during an ablation procedure. Velocity vector mapping has been proposed as an alternative method to study cardiac activation in both clinical and research environments. In addition to the visual cues that vector maps can provide, vector fields can be analyzed using mathematical operators such as the divergence and curl. In the current study, conduction features were extracted from velocity vector fields computed from cardiac mapping data. The divergence was used to locate ectopic foci and wavefront collisions, and the curl to identify central obstacles in reentrant circuits. Both operators were applied to simulated rhythms created from a two-dimensional cellular automaton model, to measured data from an in situ experimental canine model, and to complex three-dimensional human cardiac mapping data sets. Analysis of simulated vector fields indicated that the divergence is useful in identifying ectopic foci, with a relatively small number of vectors and with errors of up to 30 degrees in the angle measurements. The curl was useful for identifying central obstacles in reentrant circuits, and the number of velocity vectors needed increased as the rhythm became more complex. The divergence was able to accurately identify canine in situ pacing sites, areas of breakthrough activation, and wavefront collisions. In data from human arrhythmias, the divergence reliably estimated origins of electrical activity and wavefront collisions, but the curl was less reliable at locating central obstacles in reentrant circuits, possibly due to the retrospective nature of data collection. The results indicate that the curl and divergence operators applied to velocity vector maps have the potential to add valuable information in cardiac mapping and can be used to supplement human pattern recognition.     

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.     

Wavefront-based models for inverse electrocardiography

We introduce two wavefront-based methods for the inverse problem of electrocardiography, which we term wavefront-based curve reconstruction (WBCR) and wavefront-based potential reconstruction (WBPR). In the WBCR approach, the epicardial activation wavefront is modeled as a curve evolving on the heart surface, with the evolution governed by factors derived phenomenologically from prior measured data. The body surface potential/wavefront relationship is modeled via an intermediate mapping of wavefront to epicardial potentials, again derived phenomenologically. In the WBPR approach, we iteratively construct an estimate of epicardial potentials from an estimated wavefront curve according to a simplified model and use it as an initial solution in a Tikhonov regularization scheme. Initial simulation results using measured canine epicardial data show considerable improvement in reconstructing activation wavefronts and epicardial potentials with respect to standard Tikhonov solutions. In particular the WBCR method accurately finds the anisotropic propagation early after epicardial pacing, and the WBPR method finds the wavefront (regions of sharp gradient of the potential) both accurately and with minimal smoothing.     

Automatic quantification of changes in bone in serial MR images of joints

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.     

Model-based imaging of cardiac electrical excitation in humans

Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling ECG mapping and three-dimensional (3-D) + time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this paper, a bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts. In both patients, the AT map was reconstructed from sinus and paced rhythm data. Pacing sites were the apex of the right ventricle and the coronary sinus (CS) ostium. For CS pacing, the reconstructed AT pattern on the endocardium of the right atrium was compared with the CARTO map in both patients. The localization errors of the origins of the initial endocardial breakthroughs were determined to be 6 and 12 mm. The sites of early activation and the areas with late activation were estimated with sufficient accuracy. The reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.     

Estimation of conduction velocity vector fields from epicardialmapping data

An automated method to estimate vector fields of propagation velocity from observed epicardial extracellular potentials is introduced. The method relies on fitting polynomial surfaces T(x,y) to the space-time (x,y,t) coordinates of activity, Both speed and direction of propagation are computed from the gradient of the local polynomial surface. The components of velocity, which are total derivatives, are expressed in terms of the partial derivatives which comprise the gradient of T. The method was validated on two-dimensional (2-D) simulations of propagation and then applied to cardiac mapping data. Conduction velocity was estimated at multiple epicardial locations during sinus rhythm, pacing, and ventricular fibrillation (VF) in pigs. Data were obtained via a 528-channel mapping system from 23×22 and 24×21 arrays of unipolar electrodes sutured to the right ventricular epicardium. Velocity estimates are displayed as vector fields and are used to characterize propagation qualitatively and quantitatively during both simple and complex rhythms     

Detection of atrial activity from high-voltage leads of implantableventricular defibrillators using a cancellation technique

The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. The authors used a method that cancelled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. The authors analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICDs. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use     

Error analysis of tissue resistivity measurement

We identified the error sources in a system for measuring tissue resistivity at eight frequencies from 1 Hz to 1 MHz using the four-terminal method. We expressed the measured resistivity with an analytical formula containing all error terms. We conducted practical error measurements with in-vivo and bench-top experiments. We averaged errors at all frequencies for all measurements. The standard deviations of error of the quantization error of the 8-bit digital oscilloscope with voltage averaging, the nonideality of the circuit, the in-vivo motion artifact and electrical interference combined to yield an error of +/- 1.19%. The dimension error in measuring the syringe tube for measuring the reference saline resistivity added +/- 1.32% error. The estimation of the working probe constant by interpolating a set of probe constants measured in reference saline solutions added +/- 0.48% error. The difference in the current magnitudes used during the probe calibration and that during the tissue resistivity measurement caused +/- 0.14% error. Variation of the electrode spacing, alignment, and electrode surface property due to the insertion of electrodes into the tissue caused +/- 0.61% error. We combined the above errors to yield an overall standard deviation error of the measured tissue resistivity of +/- 1.96%.     

Termination of reentrant cardiac action potential propagation using far-field electrical pacing

Several different types of rapid cardiac rhythm disorders, including atrial and ventricular fibrillation, are likely caused by multiple, rapidly rotating, action potential (AP) waves. Thus, an electrical pacing therapy, whose effectiveness is based on being delivered with a particular timing relative to one of these waves, is unlikely to be useful in terminating the remaining waves. Here, we develop pacing protocols that are designed to terminate rotating waves independently of when the sequences of stimuli are imposed or where each wave is in its rotation at the time the sequences are initiated. These protocols are delivered as far-field stimuli, and therefore are capable of simultaneously influencing all the waves present. The pacing intervals for these protocols are, in general, of unequal duration and are determined through examination of the dynamics of AP propagation in a 1-D ring model. Series of two or three stimuli with interstimulus intervals chosen in this way are shown to be effective in terminating these waves over a wide range of ring circumferences and AP dynamical parameters. Stimulus sequences of this type may form the basis for developing new defibrillation protocols to test in experiments or more realistic models of the electrical heart.     

用方形区域内的标准正交多项式重构波前

提供了一种方形区域上归一化Zernike正交基的生成方法。它采用线性无关组Gram-Schimdt正交组构造方法,根据线性代数内积、欧氏空间及其正交性和范数的相关概念,对标准Zernike多项式进行正交处理,得到了一组新的正交多项式Z-square多项式。采用该正交基实现了方形区域内波前模式的拟合,它不仅可由Z-square模式的集合直接对波前进行表示,而且也可以通过线性反变换,将Z-square多项式表示成标准的Zernike模式的线性组合,使被分解的波前模式与像差之间有明确的对应关系。实验表明,它不仅可以对透镜设计中的波前像差函数进行有效的拟合,而且也能对Hartmann-Shack波前传感器测试得到的实际相位数据进行拟合。     

Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.     

Discrimination of atrial fibrillation from regular atrial rhythmsby spatial precision of local activation direction

This study tests the hypothesis that atrial fibrillation (AFib) can be discriminated from regular atrial rhythms by a measure of the variation in local activation direction. Human endocardial atrial recordings of AFib, sinus rhythm, atrial flutter, and supraventricular tachycardia were collected using a catheter with orthogonally placed electrodes, and the direction of each activation was calculated using methods previously described by our laboratory. Each recording was divided into segments containing 100 activations, and the spatial precision for each segment was calculated in three dimensions, as well as in each of the three two-dimensional (2-D) planes. The three-dimensional (3-D) spatial precision for 1161 segments of AFib in 11 recordings ranged from 0.09-0.85 (mean=0.45), whereas the spatial precision for 138 segments of regular rhythms in 28 recordings was ⩾0.91 in all but four instances. The 2-D spatial precision values overlapped for all rhythms. The results indicate that 3-D spatial precision of local activation direction is a useful discriminator of AFib     

Three-dimensional imaging of ventricular activation and electrograms from intracavitary recordings

Three-dimensional (3-D) mapping of the ventricular activation is of importance to better understand the mechanisms and facilitate management of ventricular arrhythmias. The goal of this study was to develop and evaluate a 3-D cardiac electrical imaging (3DCEI) approach for imaging myocardial electrical activation from the intracavitary electrograms (EGs) and heart-torso geometry information over the 3-D volume of the heart. The 3DCEI was evaluated in a swine model undergoing intracavitary noncontact mapping (NCM). Each animal's preoperative MRI data were acquired to construct the heart-torso model. NCM was performed with the Ensite 3000 system during acute ventricular pacing. Subsequent 3DCEI analyses were performed on the measured intracavitary EGs. The estimated initial sites (ISs) were compared to the precise pacing locations, and the estimated activation sequences (ASs) and EGs were compared to those recorded by the NCM system over the endocardial surface. In total, six ventricular sites from two pigs were paced. The averaged localization error of IS was 6.7 ± 2.6 mm. The endocardial ASs and EGs as a subset of the estimated 3-D solutions were consistent with those reconstructed from the NCM system. The present results demonstrate that the intracavitary-recording-based 3DCEI approach can well localize the sites of initiation and can obtain physiologically reasonable ASs as well as EGs in an in vivo setting under control/paced conditions. This study suggests the feasibility of tomographic imaging of 3-D ventricular activation and 3-D EGs from intracavitary recordings.     

基于KL距离加权和局部邻域信息的CV模型

本文提出了基于Kullback-Leibler(KL)距离加权和局部邻域信息的Chen-Vese(CV)模型.引入KL距离作为内外部局部区域能量的权值系数;计算曲线附近点的局部邻域能量之和作为模型的内部能量,从而提高对边缘的检测性能,并降低区域内灰度不均匀等因素对曲线进化的影响.验证实验采用大量实际临床数据,结果表明该...     

Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing

We propose a unified atrial fibrillation (AF)-ventricular pacing (VP) (AF-VP) model to demonstrate the effects of VP on the ventricular rhythm during atrial fibrillation AF. In this model, the AV junction (AVJ) is treated as a lumped structure characterized by refractoriness and automaticity. Bombarded by random AF impulses, the AVJ can also be invaded by the VP-induced retrograde wave. The model includes bidirectional conduction delays in the AVJ and ventricle. Both refractory period and conduction delay of the AVJ are dependent upon its recovery time. The electrotonic modulation by blocked impulses is also considered in the model. Our simulations show that, with proper parameter settings, the present model can account for most principal statistical properties of the RR intervals during AF. We further demonstrate that the AV conduction property and the ventricular rate in AF depend on both AF rate and the degree of electrotonic modulation in the AVJ. Finally, we show that multilevel interactions between AF and VP can generate various patterns of ventricular rhythm that are consistent with previous experimental observations.     

Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.     

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.