Automatically optimized vectorcardiographic features are associated with recurrence of atrial fibrillation after electrical cardioversion.

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.

Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology.

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models.

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction.

Objective: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process.

Ventricular dyssynchrony imaging, echocardiographic and clinical outcomes of left bundle branch pacing and biventricular pacing.

Background: Left bundle branch pacing (LBBP) is a novel physiological pacing technique which may serve as an alternative to cardiac resynchronization therapy (CRT) by biventricular pacing (BVP). This study assessed ventricular activation patterns and echocardiographic and clinical outcomes of LBBP and compared this to BVP.

Methods: Fifty consecutive patients underwent LBBP or BVP for CRT.