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Clinically interpretable multiclass neural network for discriminating cardiac diseases.
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 PDFAutomatically optimized vectorcardiographic features are associated with recurrence of atrial fibrillation after electrical cardioversion.
Sci 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 PDFSpatial ventricular gradient is associated with pacing-induced cardiomyopathy.
Heart 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.
The spatial ventricular gradient is associated with inducibility of ventricular arrhythmias during electrophysiology study.
Heart Rhythm
November 2024
Harvard-Thorndike Arrhythmia Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts. Electronic address:
Background: Myocardial electrical heterogeneity is critical for normal cardiac electromechanical function, but abnormal or excessive electrical heterogeneity is proarrhythmic. The spatial ventricular gradient (SVG), a vectorcardiographic measure of electrical heterogeneity, has been associated with arrhythmic events during long-term follow-up, but its relationship with short-term inducibility of ventricular arrhythmias (VAs) is unclear.
Objective: This study was designed to determine associations between SVG and inducible VAs during electrophysiology study.
Electrocardiographic Z-axis QRS-T voltage-time-integral in patients with typical right bundle branch block - Correlation with echocardiographic right ventricular size and function.
J Electrocardiol
January 2024
Department of Cardiovascular Medicine, The University of Kansas Medical Center, Kansas City, KS, United States of America. Electronic address:
Background: Right bundle branch block (RBBB) can be benign or associated with right ventricular (RV) functional and structural abnormalities. Our aim was to evaluate QRS-T voltage-time-integral (VTI) compared to QRS duration and lead V1 R' as markers for RV abnormalities.
Methods: We included adults with an ECG demonstrating RBBB and echocardiogram obtained within 3 months of each other, between 2010 and 2020.
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