Arrhythmias

An enhanced hypergraph CNN with adaptive focal loss for automated ECG heartbeat classification.

Deep learning techniques have shown significant promise for the automated diagnosis of CVD using ECG...

AI in the EP Lab - Mapping, Imaging, and Signal Interpretation.

Artificial intelligence (AI) is the use of computational models to learn from electrical, anatomical...

Predicting immune checkpoint inhibitor cardiotoxicity using machine learning: a systematic review of model performance and methodological quality.

BACKGROUND: Immune checkpoint inhibitors (ICIs) significantly improve cancer outcomes but can cause ...

A multi-branch vision transformer with GA-optimized focal loss for ECG signal classification.

Background The classification of electrocardiogram (ECG) signals is a critical task in detecting car...

Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.

OBJECTIVE: Early and accurate prediction of neurological outcomes and mortality in comatose patients...

CODE-II: a large-scale dataset for artificial intelligence in ECG analysis.

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datase...

Endurance exercise remodels pulmonary vein sleeve myocytes and promotes a proarrhythmic atrial substrate.

BACKGROUND AND AIMS: The risk of atrial fibrillation (AF) is higher in endurance athletes. Pulmonary...

Beyond Terminator Narratives: Implantable Cardioverter-Defibrillators as a Lens for Clinical Agentic Artificial Intelligence.

Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department.

AIMS: Emergency department overcrowding, especially in cardiac units, delays care and raises mortali...

Heart disease diagnosis and categorization from ECG signals using hybrid Fuzzy-CNN machine optimized by meta-heuristic algorithms.

Cardiovascular diseases are among the most important causes of global mortality, and their diagnosis...

Risk Prediction Model for Critical Illness in Connective Tissue Disease-associated Interstitial Lung Disease.

OBJECTIVE: This study was to optimize the current methods for identifying and predicting the risk of...

Multimodal Cardiovascular Disease Detection Using ECG Image and EHR.

ECG is an important signal for cardiovascular disease prediction. Since the ECG signals are often st...

Generalization of ML Models Between ECG and VCG Representation.

Integrating heterogeneous data sources is vital for developing and validating robust medical machine...

Architecture-Specific Impact of Preprocessing on Machine Learning Models for ECG Classification.

Automated analysis of electrocardiograms relies increasingly on deep learning models. In these model...

Interpreting ECG Images with Multimodal Large Language Models.

Cardiac diseases are one of the leading causes of death worldwide. Electrocardiography (ECG) is one ...

Stabilizing neuromorphic ECG processors via adaptive fractional fatigue.

Spiking Neural Networks (SNNs) deployed on wearable devices can exhibit runaway firing when processi...

FDG PET for cardiac sarcoidosis: Protocol optimization, quantification, pitfalls, and multimodality imaging integration.

Cardiac sarcoidosis (CS) is a clinically heterogeneous disorder associated with significant morbidit...

Optimization of connectome weights for a neural network model generating both forward and backward locomotion in C. elegans.

Previous studies tracking the relationship between manipulations of C. elegans neurons and the resul...

Tropical basin interactions reduce spring predictability barrier of ENSO in a deep learning model.

The El Niño-Southern Oscillation (ENSO) exhibits a pronounced decline in predictability during borea...

Relationship between the diagnostic probability of chronic kidney disease calculated using artificial intelligence-enhanced electrocardiography and the incidence of cardiovascular events.

AIMS: A low estimated glomerular filtration rate (eGFR) is the primary diagnostic criterion for chro...

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 va...