Arrhythmias

Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.

PROPOSE: An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG feat...

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment.

In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in r...

As artificial intelligence goes multimodal, medical applications multiply.

Machines don't have eyes, but you wouldn't know that if you followed the progression of deep learning models for accurate interpretation of medical im...

Artificial intelligence-enhanced electrocardiography for early assessment of coronavirus disease 2019 severity.

Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography (AI-ECG) could assist in early coronavirus diseas...

Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT.

PURPOSE/OBJECTIVE: Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated de...

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiov...

Clinical perspectives on the adoption of the artificial intelligence-enabled electrocardiogram.

The 12‑lead electrocardiogram (ECG) is a common and inexpensive diagnostic modality available at scale. The ECG reflects electrical activity throughou...

ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.

Mental health, especially stress, plays a crucial role in the quality of life. During different phases (luteal and follicular phases) of the menstrual...

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to...

Value of Imaging in the Non-Invasive Prediction of Recurrence after Catheter Ablation in Patients with Atrial Fibrillation: An Up-to-Date Review.

Catheter ablation (CA) is the first-line treatment for atrial fibrillation (AF) patients. However, the risk of recurrence associated with CA treatment...

Improving detection of obstructive coronary artery disease with an artificial intelligence-enabled electrocardiogram algorithm.

BACKGROUND AND AIMS: To evaluate the risk of coronary artery disease (CAD), the traditional approach involves assessing the patient's symptoms, tradit...

Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges.

Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. However, imbalanced datasets pose a major problem for...

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric ...

Deep-Learning-Based Metal Artefact Reduction With Unsupervised Domain Adaptation Regularization for Practical CT Images.

CT metal artefact reduction (MAR) methods based on supervised deep learning are often troubled by domain gap between simulated training dataset and re...

The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.

Catheter ablation (CA) is considered as one of the most effective methods technique for eradicating persistent and abnormal cardiac arrhythmias. Never...

Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac condit...

Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is a...

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from th...

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a gene...