In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se = 99.66% and a positive predictivity of P+ = 99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.

Visible T wave alternans (TWA) in the electrocardiogram (ECG) had been regarded as an infrequent phenomenon during the first 80 years of electrocardiography. Nevertheless, computerized analysis changed this perception. In the last two decades, a variety of techniques for automatic TWA analysis have been proposed. These techniques have allowed researchers to detect nonvisible TWA in a wide variety of clinical and experimental conditions. Such studies have recently shown that TWA is related to cardiac instability and increased arrhythmogenicity. Comparison of TWA analysis methods is a difficult task due to the diversity of approaches. In this paper, we propose a unified framework which holds the existing methods. In the light of this framework, the methodological principles of the published TWA analysis schemes are compared and discussed. This framework may have an important role to develop new approaches to this problem.

IntroductionPlasma amyloid β (Aβ) peptides have been previously studied as candidate biomarkers to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease.MethodsFree and total Aβ42/40 plasma ratios (FP42/40 and TP42/40, respectively) were determined using ABtest assays in cognitively normal subjects from the Australian Imaging, Biomarker and Lifestyle Flagship Study. This population was followed-up for 72 months and their cortical Aβ burden was assessed with positron emission tomography.ResultsCross-sectional and longitudinal analyses showed an inverse association of Aβ42/40 plasma ratios and cortical Aβ burden. Optimized as a screening tool, TP42/40 reached 81% positive predictive value of high cortical Aβ burden, which represents 110% increase over the population prevalence of cortical Aβ positivity.DiscussionThese findings support the use of plasma Aβ42/40 ratios as surrogate biomarkers of cortical Aβ deposition and enrichment tools, reducing the number of subjects submitted to invasive tests and, consequently, recruitment costs in clinical trials targeting cognitively normal individuals.

Computational Anatomy aims for the study of variability in anatomical structures from images. Variability is encoded by the spatial transformations existing between anatomical images and a template selected as reference. In the absence of a more justified model for inter-subject variability, transformations are considered to belong to a convenient family of diffeomorphisms which provides a suitable mathematical setting for the analysis of anatomical variability. One of the proposed paradigms for diffeomorphic registration is the Large Deformation Diffeomorphic Metric Mapping (LDDMM). In this framework, transformations are characterized as end points of paths parameterized by time-varying flows of vector fields defined on the tangent space of a Riemannian manifold of diffeomorphisms and computed from the solution of the non-stationary transport equation associated to these flows. With this characterization, optimization in LDDMM is performed on the space of non-stationary vector field flows resulting into a time and memory consuming algorithm. Recently, an alternative characterization of paths of diffeomorphisms based on constant-time flows of vector fields has been proposed in the literature. With this parameterization, diffeomorphisms constitute solutions of stationary ODEs. In this article, the stationary parameterization is included for diffeomorphic registration in the LDDMM framework. We formulate the variational problem related to this registration scenario and derive the associated Euler-Lagrange equations. Moreover, the performance of the non-stationary vs the stationary parameterizations in real and simulated 3D-MRI brain datasets is evaluated. Compared to the non-stationary parameterization, our proposal provides similar results in terms of image matching and local differences between the diffeomorphic transformations while drastically reducing memory and time requirements.

T-wave alternans (TWA) has been linked to increased vulnerability to ventricular fibrillation in different settings including myocardial ischemia. In this study, we propose a methodology for the characterization of TWA induced by transient, regional ischemia. We studied the prevalence, magnitude and spatio-temporal relationship between TWA and ischemia in 95 patients undergoing percutaneous transluminal coronary angioplasty (PTCA). Two electrocardiogram records of each patient, a control recording before PTCA and the PTCA record, were analyzed using a robust, recently proposed method for TWA analysis. The detected episodes were characterized in terms of their time-course, lead distribution and alternans waveform. One third of the patients (33.7%) showed TWA episodes during PTCA. The highest prevalence (51.7%) and amplitude were found in patients with left anterior descendent artery occlusion. The onset of TWA was detected after the first 1-2 min of occlusion, suggesting that some level of ischemia must be attained before TWA arises, while disappearance of TWA following reperfusion was much more rapid. The TWA lead distributions and waveforms showed distinct distributions according to the occluded artery reflecting the regional nature of the TWA phenomenon.

Abstract. This paper focuses on the estimation of statistical atlases of 3D images by means of diffeomorphic transformations. Within a LogEuclidean framework, the exponential and logarithm maps of diffeomorphisms need to be computed. In this framework, the Inverse Scaling and Squaring (ISS) method has been recently extended for the computation of the logarithm map, which is one of the most time demanding stages. In this work we propose to apply the Baker-Campbell-Hausdorff (BCH) formula instead. In a 3D simulation study, BCH formula and ISS method obtained similar accuracy but BCH formula was more than 100 times faster. This approach allowed us to estimate a 3D statistical brain atlas in a reasonable time, including the average and the modes of variation. Details for the computation of the modes of variation in the Sobolev tangent space of diffeomorphisms are also provided.

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The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in real-time monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.