Arrhythmias

The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias.

Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.

Sudden cardiac death (SCD) remains a pressing health issue, affecting hundreds of thousands each yea...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of ca...

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and ...

SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia s...

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm i...

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular d...

Model based deep learning method for focused ultrasound pathway scanning.

The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is ...

Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

PURPOSE: To develop and compare 3 different machine learning-based models of clinical information an...

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal ana...

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze intervention...

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affec...

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. Thi...

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match o...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challen...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defin...

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its ...

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have bee...

Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant ...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker env...

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection a...