Arrhythmias

Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects.

BACKGROUND: Arrhythmia is a frequent complication following transcatheter device closure of perimembranous ventricular septal defects (pmVSD). However, there is currently a lack of a convenient tool for predicting postoperative arrhythmia.

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.

BACKGROUND: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due...

MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and ma...

Predicting lack of clinical improvement following varicose vein ablation using machine learning.

OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in ...

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage ar...

Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within...

Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.

BACKGROUND: Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time c...

Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.

OBJECTIVES: To evaluate the effectiveness of an MRI radiomics stacking ensemble learning model, which combines T2-weighted imaging (T2WI) and contrast...

Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of cath...

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in c...

Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.

BACKGROUND: Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) in hypertrophic cardiomyopathy (HCM) typically represents myocardial...

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased sym...

Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this ...

Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints.

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation has traditionally relied on cardiologists' exper...

Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.

BACKGROUND: Large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT) excel at interpreting unstructured data from public...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who...

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.

BACKGROUND: Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficiency, scalability, and insight generation. In cardiol...

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neu...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worl...