The Authors have submitted to an electrocardiographic, vectorcardiographic, mechanocardiographic and echocardiographic investigation 4 cases with Duchenne's disease, which had already been studied by the Authors several years before. The longitudinal study has demonstrated, above all, the striking capacity of the electrocardiographic aspects of the disease to evolve from a normal to a "pseudo-necrotic" pattern. Such evolution, among other things, provides an important argument against the interpretation that attributes the electrocardiographic and vectorcardiographic changes in the initial stages of the disease to a persistence of a QRS loop of infantile type on a genetic basis. But for rare exceptions, the systolic time intervals and kinetocardiogram, which showed early indicative changes on the first examination, have successively shown easily predictable behavior considering the poor cardiovascular conditions of the patients on the second examination. The echocardiogram has proved useful in demonstrating the morphological and functional changes of the ventricular walls and of the interventricular septum, besides the eventual associated mitral valve prolapse. The echocardiographic evaluation of the left ventricular performance in quantitative terms, however, seems somewhat unreliable owing to the difficulty of obtaining technically good images, due to the thoracic deformity. The dystrophic changes recently observed in the myocardium even at ultrastructural level can probably explain not only the electrocardiographic and vectorcardiographic abnormalities but also the kinetocardiographic and echocardiographic changes. Among the above mentioned theoretical and practical considerations the possibility should be underlined that some cases of cardiomyopathy labelled as "primary" are in fact unrecognized dystrophic cardiomyopathies.
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Heliyon
January 2025
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, Ancona, 60131, Italy.
Background: Deep-learning applications in cardiology typically perform trivial binary classification and are able to discriminate between subjects affected or not affected by a specific cardiac disease. However, this working scenario is very different from the real one, where clinicians are required to recognize the occurrence of one cardiac disease among the several possible ones, performing a multiclass classification. The present work aims to create a new interpretable deep-learning tool able to perform a multiclass classification and, thus, discriminate among several different cardiac diseases.
View Article and Find Full Text PDFSci Rep
January 2025
Cardiocenter, Third Faculty of Medicine, Charles University and University Hospital Kralovske Vinohrady, Prague, Czech Republic.
Electrical cardioversion presents one of the treatment options for atrial fibrillation (AF). However, the early recurrence rate is high, reaching ~40% three months after the procedure. Features based on vectorcardiographic signals were explored to find association with early recurrence of AF.
View Article and Find Full Text PDFHeart Rhythm
December 2024
Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts. Electronic address:
Background: Pacing-induced cardiomyopathy (PICM) is a frequent complication of right ventricular pacing that often requires reoperation for biventricular or conduction system pacing. Better methods for predicting PICM may inform initial pacing strategy and follow-up monitoring.
Objective: The purpose of this study was to determine whether the spatial ventricular gradient (SVG), a vectorcardiographic marker of ventricular electrical and mechanical heterogeneity, is associated with the subsequent development of PICM.
J Electrocardiol
December 2024
Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, United States of America. Electronic address:
Physiol Meas
July 2024
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.
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