Arrhythmias

Predictors of atrial fibrillation recurrence after catheter ablation: a systematic review.

BACKGROUND: Recurrence of atrial fibrillation (AF) following catheter ablation is a major clinical problem, and there is considerable variability in patient outcomes. The identification of reliable predictors of AF recurrence is critical in developing patient management strategies. OBJECTIVE: To systematically identify and synthesize evidence on independent predictors of atrial fibrillation recurr...

Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.

BACKGROUND: Machine learning models for Obstructive Sleep Apnea (OSA) diagnosis have largely inherited some structural limitations: reliance on generic, opportunistically collected feature sets; use of the Apnea-Hypopnea Index (AHI) as the sole ground truth; poor performance in multi-class severity grading; and predictions that offer clinicians no mechanistic insight. This study addresses these ga...

Conformal reliability assessment of PPG-based ICU arrhythmia classification under class imbalance and patient heterogeneity.

Deep learning models for photoplethysmography (PPG)-based arrhythmia detection in intensive care are often evaluated by average accuracy, while confor...

[Diagnostic performance of a dual-stage deep learning model based on cone beam CT-derived multi-view two-dimensional slices for assessing ankylosed impacted teeth].

Objective: To develop and validate a dual-layer deep learning model based on cone-beam CT (CBCT)-derived multi-view two-dimensional (2D) slices for th...

Wellens' Sign and Syndrome Detected With the Home Use of a Novel 12-Lead Electrocardiogram.

BACKGROUND: Wellens' sign is a high-risk electrocardiogram (ECG) pattern associated with proximal left anterior descending artery stenoses and high ri...

Stepwise increasing input to machine learning models predicting 30-day AMI or death in emergency department chest-pain patients.

Introduction This study evaluated the performance of neural network (NN) models with stepwise increasing input for identifying acute myocardial infarc...

Use of heart rate variability in prediction of clinical deterioration in critically ill hospitalised adults: a systematic review protocol.

INTRODUCTION: Heart rate variability (HRV) is a measurement derived from the beat-to-beat variation in heart rate and reflects the complex interaction...

Towards Explainability in Deep Learning for Detection of Five Major Intracranial Hemorrhage Subtypes on Head CT Using Multi-window DICOM Imaging and Patient-Level Cross-Validation.

Intracranial hemorrhage (ICH) is a time-critical neurologic emergency where delayed diagnosis can worsen outcomes. Non-contrast head CT is the first-l...

A lightweight hybrid framework integrating convolutional neural networks and fast Fourier transform for reliable and calibrated ECG-based cardiac abnormality detection.

The electrocardiogram (ECG) is an essential non-invasive tool for detecting cardiac abnormalities; however, accurate interpretation often requires spe...

Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records.

BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospe...

ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequ...

Interpretable Dual-Stream EEG-MRI Fusion Uncovers Structure-Function Signatures of Stroke Motor Recovery.

Motor recovery prediction after stroke is hindered by the inability of single-modality imaging to capture how structural damage and functional reorgan...

[Systems saving lives: a systems-based approach to cardiac arrest management].

Out-of-hospital cardiac arrest remains a major public health challenge worldwide, with survival largely determined by the timeliness, quality, and org...

Robust stress detection from wearable ECG using subject-specific normalization and stacking ensemble learning.

Driven by the rising prevalence of mental health issues, this study proposes an automated stress monitoring system using wearable ECG signals. Designe...

R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.

BACKGROUND: Long-term garment-type wearable Holter electrocardiographic (ECG) monitoring is frequently affected by noise contamination, which complica...

Direct Reconstruction of High-Fidelity Electrocardiogram Signals From Vector-Based PDF Files With Integrated Deep Learning for Multiparameter Estimation: Retrospective Methodological Study.

BACKGROUND: Electrocardiograms (ECGs) are commonly stored in PDF, particularly as vector-based files generated by ECG management systems. Previous stu...

Leipzig Heart Center ECG-database: Arrhythmias in children and patients with congenital heart disease.

Interpretation of Electrocardiogramms (ECG) is increasingly complemented by algorithms. These algorithms are based on large datasets. Here we pre...

Intelligent recognition and segmentation of anatomical structures in spinal endoscopy: a deep learning approach with 1000 annotated images.

BACKGROUND: Accurate intraoperative identification of anatomical structures is critical for ensuring the safety and efficacy of spinal endoscopic surg...

AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) fr...

Prediction of heart arrhythmia onset in patients with ICD using machine learning algorithms.

PURPOSE: The current study uses machine learning algorithms to predict the onset of cardiac arrhythmia in patients with implantable cardioverter-defib...