Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.

Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its recurrence. Nowadays, clinicians cannot differentiate a priori between the different subtypes, and patient classification is done afterwards, when its clinical course is available. In this paper we present a comparison of classification performances when differentiating paroxysmal and persistent atrial fibrillation episodes by means of support vector machines. We analyze short surface electrocardiogram recordings by extracting modulus and phase features from several time-frequency transforms: short-time Fourier transform, Wigner-Ville, Choi-Williams, Stockwell transform, and general Fourier-family transform. Overall, accuracy higher than 81% is obtained when classifying phase information features of real test ECGs from a heterogeneous cohort of patients (in terms of progression of the arrhythmia and antiarrhythmic treatment) recorded in a tertiary center. Therefore, phase features can facilitate the clinicians' choice of the most appropriate treatment for each patient by means of a non-invasive technique (the surface ECG).

Intrauterine Growth Restriction (IUGR) and premature birth are associated with higher risk of cardiovascular diseases throughout adulthood. The aim of this study was to evaluate the influence of these factors in ventricular electrical remodeling in preadolescents. Electrocardiography was performed in a cohort of 33-IUGR, 32-preterm with appropriate weight and 60 controls. Depolarization and repolarization processes were studied by means of the surface ECG, including loops and angles corresponding to QRS and T-waves. The angles between the dominant vector of QRS and the frontal plane XY were different among the study groups: controls [20.03°(10.11°-28.64°)], preterm [25.48°(19.79°-33.56°)], and IUGR [27.77°(16.59°-33.23°)]. When compared to controls, IUGR subjects also presented wider angles between the difference of QRS and T-wave dominant vectors and the XY-plane [5.28°±12.15° vs 0.49°±14.15°, p<0.05] while preterm ones showed smaller frontal QRS-T angle [4.68°(2.20°-12.89°) vs 6.57°(2.72°-11.31°), p<0.05]. Thus, electrical remodeling is present in IUGR and preterm preadolescents, and might predispose them to cardiovascular diseases in adulthood. Follow-up studies are warranted.

Random variables in biology, social and health sciences commonly follow skewed distributions. Many of these variables can be represented by exGaussian functions; however, in practice, they are sometimes considered as Gaussian functions when statistical analysis is carried out. The asymmetry can play a fundamental role which can not be captured by central tendency estimators such as the mean. By means of Monte Carlo simulations, the effect of a small asymmetry in the generating functions of the chi distribution is studied. To this end, the k generating functions are taken as exGaussian functions. The limits of this approximation are tested numerically for the practical case of three health-related variables: one physical (body mass index) and two cognitive (verbal fluency and short-term memory). This work is in line with our previous works on a physics-inspired mathematical model to represent the reaction times of a group of individuals.