Arrhythmias

Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound.

AIMS: Biological age is increasingly recognized as a superior predictor of morbidity, mortality, compared with chronological age. Artificial intelligence (AI)-driven ageing clocks enable rapid, non-invasive assessment. Cardiovascular (CV) ageing is of particular relevance given its central role in systemic metabolic health. This study evaluated the clinical utility of an ultrasound (US)-based CV b...

Advances in Electrocardiogram-Based Non-Invasive Blood Glucose Monitoring Technology.

Blood glucose monitoring is fundamental to diabetes management, yet traditional invasive methods are limited by patient discomfort and infection risks. In recent years, electrocardiogram (ECG), a conventional tool for cardiovascular assessment, has gained attention as a prospective method for non-invasive blood glucose monitoring. The underlying principle is that glycemic fluctuations can modulate...

Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study.

BACKGROUND: Predictive medicine relies on algorithms to determine clinical treatments tailored to each patient's individual characteristics. Predictiv...

Towards real-time sleep stage classification: A deep learning approach leveraging PPG and ECG.

OBJECTIVE: This work aims to enable adaptive Consumer Sleep Technologies (CSTs) for sleep intervention by developing a deep learning model for sleep s...

An attention-based multimodal deep learning framework integrating EEG and ECG for enhanced stress detection.

Chronic stress is an important threat in Public Health, as it negatively impacts both the Body and Mind. Current methods for measuring and identifying...

Artificial Intelligence Enabled Noninvasive Mapping of Cardiac Arrhythmia Origins Using the 12-Lead Electrocardiogram.

Artificial intelligence (AI) is reshaping cardiac electrophysiology by extracting information from electrocardiograms that exceeds human visual interp...

Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remain...

Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices.

In this paper, we present a memory-efficient ECG based heartbeat classification for wearable devices enabled by multi-feature fusion and compressed bi...

Integrating traditional statistics and explainable machine learning for risk stratification of atrial fibrillation recurrence after pulsed field ablation: a hypothesis-generating single-center study.

BACKGROUND: Predicting recurrence after pulsed field ablation (PFA) for paroxysmal atrial fibrillation (AF) remains challenging, particularly in early...

Recent developments in pharmacophore-based modeling of Ca2+/Calmodulin-dependent protein kinase II delta (CaMkIIδ) inhibitors for heart failure therapy.

INTRODUCTION: Calcium/calmodulin-dependent protein kinase II delta (CaMKIIδ) regulates cardiac excitation - contraction coupling and contributes to he...

Why nonlinear models matter: unified analysis of cognitive load, stress, and exercise using wearable physiological signals.

\textbf{Objective:} Physiological measurements obtained from wearable devices reflect complex autonomic nervous system dynamics that are often ass...

Beyond recurrence: redefining atrial fibrillation burden as a prognostic and therapeutic endpoint.

Atrial fibrillation (AF) has traditionally been classified by episode duration, whereas rhythm control outcomes-via antiarrhythmic drugs or catheter a...

Multimodal MRI radiomics for predicting HIFU ablation efficacy in uterine fibroids: a machine learning study.

OBJECTIVE: To explore the predictive value of machine learning-based multimodal MRI radiomics combined with clinical features in the efficacy of high-...

Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification.

AIMS: Attribution-based explainability methods are widely used in electrocardiogram (ECG) analysis to interpret predictions from 'black-box' deep neur...

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of di...

Counted Fingerprint-Enhanced Graph Neural Network Models Enable Accurate Screening of hERG Blockers from Diverse Categories of Chemicals.

Chemical-induced blockade of the human ether-a-go-go-related gene (hERG) K+ channels may lead to fatal cardiac arrhythmia. Given the ever-increasing n...

Prediction of Heart Failure With Reduced Ejection Fraction With Artificial Intelligence Electrocardiography in Patients With Atrial Fibrillation.

OBJECTIVE: To determine whether a low ejection fraction artificial intelligence electrocardiogram (AI-ECG) algorithm predicts incident heart failure w...

Accuracy of GPT-5 and GPT-4o in diagnosing STEMI from 12-Lead ECGs: A comparative study with cardiologists and emergency physicians.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) requires rapid, accurate electrocardiogram (ECG) interpretation. The diagnostic effecti...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Deep Learning-Based ROSC Prediction and ECG Phenotyping in Out-of-Hospital Cardiac Arrest.

INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been assoc...