An anthropomorphic phantom for atrial transseptal puncture simulation training.

Background: Transseptal puncture (TSP) is a critical prerequisite for left-sided cardiac interventions, such as atrial fibrillation (AF) ablation and left atrial appendage closure. Despite its routine nature, TSP can be technically demanding and carries a risk of complications. This study presents a novel, patient-specific, anthropomorphic phantom for TSP simulation training that can be used with X-ray fluoroscopy and ultrasound imaging.

Deep Learning for Pediatric Sleep Staging from Photoplethysmography: A Transfer Learning Approach from Adults to Children.

Background: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedures. Recent advancements in data-driven algorithms using photoplethysmogram (PPG) time series have shown promise in automating sleep staging in adults.

An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.

Aims: Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm by leveraging a continuous 24 h Holter electrocardiogram and artificial intelligence.

Predictors of the efficacy of His bundle pacing in patients with a prolonged PR interval: A stratified analysis of the HOPE-HF randomized controlled trial.

Aims: The randomized, double-blind, placebo-controlled HOPE-HF trial assessed the benefit of atrio-ventricular (AV) delay optimization delivered using His bundle pacing. It recruited patients with left ventricular ejection fraction ≤40%, PR interval ≥200 ms, and baseline QRS ≤140 ms or right bundle branch block. Overall, there was no significant increase in peak oxygen uptake (VOmax) but there was significant improvement in heart failure specific quality of life.

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited.