Arrhythmias

Artificial intelligence for individualized treatment of persistent atrial fibrillation: a randomized controlled trial.

Although pulmonary vein isolation (PVI) has become the cornerstone ablation procedure for atrial fib...

Transformer-based heart language model with electrocardiogram annotations.

This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillati...

An arrhythmia classification using a deep learning and optimisation-based methodology.

The work proposes a methodology for five different classes of ECG signals. The methodology utilises ...

Active learning and margin strategies for arrhythmia classification in implantable devices.

BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardi...

An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.

BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medi...

Spherical lesion formation in HIFU using robotic assistance for controlled focal point manipulation.

We propose a robot-assisted method to generate spherical thermal lesions by high-intensity focused u...

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachyc...

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity...

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of ...

AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the ...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring.

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as...

Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.

Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data t...

Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.

BACKGROUND: Although catheter ablation (CA) is currently the most effective clinical treatment for a...

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respon...

SleepECG-Net: Explainable Deep Learning Approach With ECG for Pediatric Sleep Apnea Diagnosis.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to...

[Investigation of impedance drop during lesions created by robot-assisted radiofrequency ablation].

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate int...

CaBind_MCNN: Identifying Potential Calcium Channel Blocker Targets by Predicting Calcium-Binding Sites in Ion Channels and Ion Transporters Using Protein Language Models and Multiscale Feature Extraction.

Calcium ions (Ca) are crucial for various physiological processes, including neurotransmission and c...

Multi-modal dataset creation for federated learning with DICOM-structured reports.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data stor...

Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.

Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wave...

Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.

The bidirectional interactions between brain and heart through autonomic nervous system is the prime...