Arrhythmias

Can atrial fibrillation ablation outcomes be properly predicted with electrocardiography and artificial intelligence?

AIMS: The success of ablation for atrial fibrillation (AF) varies, often leading to repeat ablation. Reliable prediction of repeat ablation remains challenging. This study aimed to investigate if AF ablation outcomes can be predicted with an electrocardiogram (ECG)-based deep learning (DL) algorithm. METHODS AND RESULTS: We included 865 patients undergoing AF ablation, of whom 163 (18.8%) required...

[Cardiology : what's new in 2025].

In 2025, significant progress has been made in the management of heart failure and cardiovascular diseases, driven by the emergence of new treatments whose effectiveness is now well-established. In cardiac imaging, artificial intelligence-enhanced MRI has become the reference examination for evaluating myopericardial syndromes, while photon-counting CT has markedly improved the assessment of coron...

Computerized Self-Reported Medical History Taking to Support Early Rule Out of Major Adverse Cardiac Events in Patients With Acute Chest Pain: Post Hoc Analysis of the CLEOS-CPDS Prospective Cohort Study.

BACKGROUND: Self-reported, computerized history taking (CHT) may enable efficient collection of medical histories for acute chest pain management. OBJ...

Stress Detection Using Heart Rate Variability and Respiratory Signals Derived From a Single-Lead ECG.

Stress detection is a widely studied field due to its significant implications for mental and physical health. While multimodal approaches show promis...

A novel approach for atrial fibrillation-related obstructive sleep apnea detection using enhanced single-lead electrocardiogram features with customized deep learning algorithm.

STUDY OBJECTIVES: Atrial fibrillation (AF) and obstructive sleep apnea (OSA) are interrelated conditions that substantially increase the risk of cardi...

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Un...

MM-GradCAM: an improved multimodal GradCAM method with 1D and 2D ECG data for detection of cardiac arrhythmia.

As cardiac arrhythmia remains one of the leading causes of death worldwide, early and accurate diagnosis of cardiac arrhythmia is critical to improvin...

Dynamic prediction of paroxysmal atrial fibrillation onset using longitudinal sample entropy in joint models.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with a five-fold increased risk of stroke. Early predicti...

Screening for Tumor Microtube-Targeting Drugs Identifies PKC Modulators as Multipotent Inhibitors of Glioblastoma Progression.

UNLABELLED: Glioblastomas are incurable primary brain tumors that depend on neural-like cellular processes, tumor microtubes (TM), to invade the brain...

Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System Without Electroencephalography.

STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-in...

A cascaded CNN-LSTM framework for quantifying respiratory motion from surface electromyographic signals.

OBJECTIVE: Surface electromyographic (sEMG) signals of the diaphragm provide a valuable physiological signal for real-time respiratory monitoring, par...

Evaluation and application of electrocardiographic age model for children.

ECG-age, derived from ECG signals using deep neural networks (DNNs), correlates with health status but has been predominantly studied in adults, negle...

xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification.

The clinical deployment of artificial intelligence (AI) solutions for assessing cardiovascular disease (CVD) risk in 12-lead electrocardiography (ECG)...

A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.

Electrocardiography is a cornerstone in the diagnosis of cardiovascular diseases; however, accurate interpretation demands expert knowledge and is oft...

Deep learning for estimating right ventricular function from routine coronary angiography.

AIMS: Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sough...

LSTM-GPT-4 Integration for Interpretable Biomedical Signal Classification.

BACKGROUND: Approximately 3.8 billion people lack access to essential health services, and diagnostic interpretation remains a major bottleneck in rem...

A novel ECG QRS complex detection algorithm based on dynamic Bayesian network.

Accurate detection of the QRS complex, a crucial reference for heartbeat localization in electrocardiogram (ECG) signals, remains inadequate in wearab...

Ultrasound-guided microwave ablation for breast tumors: current status and future perspectives.

Ultrasound (US)-guided microwave ablation (MWA) has emerged as a promising minimally invasive therapy for both benign and malignant breast tumors. Thi...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation and Clinical Outcomes in Kidney Transplant Recipients.

BACKGROUND: Incident atrial fibrillation (AF) is common following kidney transplantation (KTx) and is associated with worse clinical outcomes. Artific...

Early heart-rate trajectory phenotypes predict short-term mortality in critically ill patients: a dynamic time-warping cluster analysis.

UNLABELLED: Heart rate (HR) reflects illness severity in critically ill patients, but the prognostic significance of early HR changes is unclear. We a...