Arrhythmias

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 to achieve both high accuracy and real-time performance under noisy, imbalanced conditions. We propose WaveMamba-Net, a deep learning framework integrating wavelet-based multi-scale decomposition with state-space modeling for patient status classifi...

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 developed an AI-based model integrating routine data and automated ECGs to improve early risk classification. METHODS AND RESULTS: This retrospective cross-sectional study involved 600 medical records of patients presenting with suspected cardiac symptoms. ...

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 ...

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 diagnosi...

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 E...

[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...