Arrhythmias

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 cardiac arrhythmias. However, challenges such as class imbalance and the need to capture both local and global temporal patterns make this problem complex.ObjectiveIn this study, a hybrid deep learning model, called MBViT-FocalGA, is proposed for the classification of electrocardiogram ECG signals.Meth...

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 after cardiac arrest remains challenging. Multimodal data integrating heart and brain electrophysiological signals may improve prognostic accuracy, but distinct predictive patterns underlying neurological recovery versus survival are not well characterized. METHODS: We analyzed 331 patients from th...

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

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligenc...

DNA demethylation of ANXA4 is associated with atrial fibrillation risk through myeloid immune mechanisms: evidence from Mendelian randomization and multi-omics analyses.

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia affecting millions of patients globally. While epigenetic modifications play a significant...

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 vein isolation (PVI) is effective in this group, ...

Advances in machine learning for cardiac event prediction: where can we still improve?

INTRODUCTION: Artificial intelligence (AI) is playing a transformative role in cardiovascular care by enabling more precise prediction of adverse clin...

WaveMamba-Net: Dual-frequency adaptive wavelet state-space network for real-time pulse signal classification in wearable health monitoring.

Continuous cardiovascular monitoring via wearable devices is critical for early disease detection, yet existing pulse signal analysis methods struggle...

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 mortality. Conventional triage is error-prone. We develop...

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 is mainly based on ECG signals. The complexity an...

Advancing stroke prevention in atrial fibrillation: a systematic review of machine learning-based risk prediction models.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and confers a four to fivefold increase in ischemic stroke risk, ...

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 critical illness in patients with connective tiss...

Multimodal Cardiovascular Disease Detection Using ECG Image and EHR.

ECG is an important signal for cardiovascular disease prediction. Since the ECG signals are often stored as images in clinical practice, we transforme...

Generalization of ML Models Between ECG and VCG Representation.

Integrating heterogeneous data sources is vital for developing and validating robust medical machine learning models. Although the 12-lead format is s...

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

Automated analysis of electrocardiograms relies increasingly on deep learning models. In these models, preprocessing steps may often be applied under ...

Interpreting ECG Images with Multimodal Large Language Models.

Cardiac diseases are one of the leading causes of death worldwide. Electrocardiography (ECG) is one of the major diagnostic methods to detect cardiac ...

Stabilizing neuromorphic ECG processors via adaptive fractional fatigue.

Spiking Neural Networks (SNNs) deployed on wearable devices can exhibit runaway firing when processing noisy electrocardiogram (ECG) signals, increasi...

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

Cardiac sarcoidosis (CS) is a clinically heterogeneous disorder associated with significant morbidity and mortality, including heart failure, conducti...

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 resulting behavioral changes have called for the develo...

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 boreal spring, referred to as spring predictability bar...

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 chronic kidney disease (CKD), a known risk factor for ...