Arrhythmias

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.

Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and ac...

Impact of ECG Characteristics on the Performance of an Artificial Intelligence Enabled ECG for Predicting Left Ventricular Dysfunction.

The application of deep learning in electrocardiogram: Where we came from and where we should go?

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versu...

Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms.

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the ...

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation...

AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model.

The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG...

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-base...

An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the 'Turing test'?

OBJECTIVE: To develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm capa...

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 ...

Automated ECG classification based on 1D deep learning network.

The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from electrodes...

A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network.

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing during slee...

A deep learning approach for 2D ultrasound and 3D CT/MR image registration in liver tumor ablation.

BACKGROUND AND OBJECTIVE: Liver tumor ablation is often guided by ultrasound (US). Due to poor image...

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile devi...

Interpretable heartbeat classification using local model-agnostic explanations on ECGs.

Treatment and prevention of cardiovascular diseases often rely on Electrocardiogram (ECG) interpreta...

Accessory pathway analysis using a multimodal deep learning model.

Cardiac accessory pathways (APs) in Wolff-Parkinson-White (WPW) syndrome are conventionally diagnose...

Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis.

To evaluate the performance of the classic machine learning algorithms and the effectiveness of vari...

Detecting Digoxin Toxicity by Artificial Intelligence-Assisted Electrocardiography.

Although digoxin is important in heart rate control, the utilization of digoxin is declining due to ...

A New ECG Denoising Framework Using Generative Adversarial Network.

This paper presents a novel Electrocardiogram (ECG) denoising approach based on the generative adver...

CEFEs: A CNN Explainable Framework for ECG Signals.

In the healthcare domain, trust, confidence, and functional understanding are critical for decision ...

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier.

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial isch...

Analysis of Potential for User Errors in Mobile Deployment of Radiology Deep Learning for Cardiac Rhythm Device Detection.

We examine how convolutional neural networks (CNNs) for cardiac rhythm device detection can exhibit ...