Arrhythmias

A reproducible benchmark of QRS detection algorithms across diverse ECG datasets and noise conditions.

Accurate R-peak detection in electrocardiograms is critical for heart rate monitoring, heart rate variability analysis, and cardiac condition diagnosis. However, reliable detection remains challenging in real-world scenarios due to noise, artifacts, and signal variability. A key limitation in current research is the lack of reproducibility and comparability, as algorithms are often tested on varyi...

Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.

Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead ECGs to monitor rhythm abnormalities, recent studies show utility in detecting myocardial diseases when combined with artificial intelligence (AI). However, the optimal single-lead axis for detecting myocardial diseases beyond rhythm abnormality remai...

[Position paper of the German Society of Cardiology-quality criteria for performing catheter ablation of atrial fibrillation: executive summary].

The updated German Society of Cardiology (DGK) position paper on catheter ablation of atrial fibrillation (AF) [1] presents the current evidence, tech...

A Multimodal Learning Framework for Detecting Systemic Hypertension in Sleep Apnea Patients Using ECG and PPG Signals.

Systemic hypertension (HTN) is a major cardiovascular comorbidity in patients with obstructive sleep apnea (OSA), yet these conditions are often diagn...

A machine learning surrogate model for fast approximation of simulated microwave ablation zones.

Microwave ablation (MWA) is a minimally invasive therapy for liver, lung, and kidney tumors. Computational modeling with finite element methods (FEM) ...

Using clinical and thrombus characteristics to predict the etiology of ischemic stroke: An analysis of the INSIGHT registry.

BACKGROUND AND PURPOSE: Ischemic stroke comprises about 87% of all stroke cases in the US. 20% of these have a cardioembolic (CE) etiology, and 25% ar...

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...

EdgeECG: a lightweight edge-oriented network with dual criterion pruning for real-time ECG arrhythmia classification.

Objective.Miniature electrocardiogram (ECG) devices can rapidly and accurately acquire real-time cardiac signals, enabling timely warnings for patient...

Cardiac activity monitoring for the heart rate classification with enhanced BiLSTM model.

Cardiac activity monitoring is important for assessing cardiovascular status and supporting computational analysis of heart-rate pattern variations fr...

Accelerated biological aging in bipolar disorder as determined by artificial intelligence-based electrocardiographic assessment.

BACKGROUND: Individuals with bipolar disorder (BD) are at increased risk for major adverse cardiovascular events. Recent evidence suggests that the di...

Artificial intelligence in the differential diagnosis of hypertrophic cardiomyopathy and physiological hypertrophy: a scoping review.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is the most common inherited myocardial disorder and a major cause of sudden cardiac death in young adul...

Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department.

BACKGROUND AND AIMS: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusi...

Signal-Based Assessment of Postural Stability Using ECG, EMG, and Center of Pressure Measures.

Postural stability reflects the integrated function of autonomic, neuromuscular, and postural control systems and deteriorates with aging and reduced ...

Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy.

BACKGROUND: Electrocardiogram (ECG) interpretation is a critical yet challenging skill for nurses. Generative artificial intelligence (AI) offers pote...

Machine learning-guided risk stratification for Long QT Syndrome genetic variants with hiPSC-derived cardiomyocytes.

AIMS: Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms a...

Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.

PURPOSE: To evaluate the performance of a Transformer-based U-Net, TransDisCo, for distortion correction in clinical diffusion-weighted imaging (DWI),...

ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.

BACKGROUND: Scalable risk stratification for ischemic stroke remains an unmet need. OBJECTIVES: In this study, the authors sought to assess whether de...

Prediction of postoperative desaturation and bradycardia in neonates undergoing laparoscopic inguinal hernia repair: a retrospective cohort study.

BACKGROUND: Ex-premature infants have a high risk of postoperative apnea and bradycardia. This study aimed to develop a predictive model for postopera...

Diagnostic Performance Evaluation of Clinical and AI Risk Models in Patients Referred for Cardiac Amyloidosis Testing.

BACKGROUND: To improve screening for cardiac amyloidosis (CA), several models using artificial intelligence (AI) and conventional statistics have been...

A deep learning ECG model for identification and localization of occlusion myocardial infarction.

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria m...