Arrhythmias

Detection of coronary microvascular dysfunction based on machine learning algorithm with multidimensional temporal-spatial features from electrocardiogram.

Coronary microvascular dysfunction (CMD) causes myocardial ischemia and is associated with adverse cardiovascular events. This study explored the value of multidimensional electrocardiogram (ECG) features in capturing pathological changes linked to CMD. Global electrical heterogeneity (GEH) parameters were extracted from vectorcardiogram (VCG) signals synthesized from ECGs o 82 CMD patients and 25...

ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.

Cardiovascular diseases are the leading cause of death in the world, requiring the accurate and timely detection of arrhythmias to prevent sudden cardiac death. In this work, ScaHybNet, a deep learning ensemble model is proposed for multi-class arrhythmia classification using the widely adopted ECG Heartbeat Categorization Dataset. The dataset comprises 109,446 samples across five heartbeat classe...

Multimodal Ultrasound Integration Pathways and Paradigm Innovations in Precision Diagnosis and Treatment of Thyroid Cancer.

RATIONALE AND OBJECTIVES: Thyroid cancer, the fastest-growing endocrine malignancy, is shifting from morphological evaluation to molecular-functional ...

AI-based Histologic Heterogeneity of Microvascular Obstruction at Cardiac MRI for Predicting MACEs: A Multicenter Study.

Background Microvascular obstruction (MVO) is strongly associated with adverse outcomes after ST-segment elevation myocardial infarction (STEMI). Howe...

Characterizing Central-Autonomic Dynamics During an Episodic Memory Task Using Multi-Modal Neural and Cardiomechanical Signals.

OBJECTIVE: Memory function underlies mental and behavioral health. While the role of the central nervous system (CNS) during episodic memory encoding ...

Cardiovascular Disease Classification System With ECG-Gating PCG Algorithm and Programmable AI Accelerator Design.

Cardiovascular diseases (CVDs) are among the leading causes of mortality. Traditional diagnostic methods require hospital visits and professional medi...

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing.

The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are ...

CEUS-Based Microvascular Invasion Predictor in HCC: Improving Prognostic Stratification Following Thermal Ablation.

BACKGROUND & AIMS: Microvascular invasion (MVI) critically impacts hepatocellular carcinoma (HCC) management. We aimed to develop and validate a deep ...

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: A loperamide case study.

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays...

Long-term continuous monitoring of wearable ECG signals for atrial fibrillation detection.

Atrial Fibrillation (AFib) is the most common sustained cardiac arrhythmia and is associated with substantial morbidity and mortality, including incre...

MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging...

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.

BACKGROUND: Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted acce...

CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.

Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream model...

Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning.

Cardiac arrhythmia is a disorder caused by disruptions in the regular heart rhythm. Arrhythmias are categorized into two classes: sinus and non-sinus ...

Fully Biobased, Robust, and High-Conductivity Hydrogel for High-Fidelity Electrophysiological Monitoring and Deep Learning-Assisted Stroke Rehabilitation.

Developing sustainable bioelectronics that simultaneously integrate mechanical robustness, high conductivity, biocompatibility, and system-level funct...

Arrhythmia Burden and Clinical Responses Under Continuous Monitoring in Heart Failure: Observations From the ALLEVIATE-HF Trial.

BACKGROUND: Arrhythmia burden in ambulatory patients with symptomatic heart failure (HF) without cardiac implantable electronic devices (CIEDs) is not...

Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.

BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress di...

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 analysis. Nevertheless, several critical challeng...

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

Artificial intelligence (AI) is the use of computational models to learn from electrical, anatomical and imaging data to assist or automate interpreta...

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 rare, potentially fatal cardiotoxicity, including ...