Arrhythmias

Perioperative and Survival Outcomes of Patients Treated With Robot-Assisted Partial Nephrectomy and Percutaneous Microwave Ablation for Small Renal Masses: A Single Center Experience.

BACKGROUND: Robot assisted partial nephrectomy (RAPN) and microwave ablation (MWA) are 2 of the most...

Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual as...

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorit...

Artificial Intelligence ECG Analysis in Patients with Short QT Syndrome to Predict Life-Threatening Arrhythmic Events.

Short QT syndrome (SQTS) is an inherited cardiac ion-channel disease related to an increased risk of...

Outcomes of salvage robot-assisted radical prostatectomy in patients who had primary focal versus whole-gland ablation: a multicentric study.

In the present study, we present comparative outcomes of radical prostatectomy after whole-gland the...

Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks.

Although numerous R-peak detectors have been proposed in the literature, their robustness and perfor...

Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

AIMS: Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mor...

Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals.

BACKGROUND: Cardiac arrhythmia is a cardiovascular disorder characterized by disturbances in the hea...

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 soug...

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 opp...

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 asym...

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

Despite challenges in severity scoring systems, artificial intelligence-enhanced electrocardiography...

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 r...

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 electrica...

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 sca...

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 phas...

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, t...

Explainable-by-design: Challenges, pitfalls, and opportunities for the clinical adoption of AI-enabled ECG.

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...

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

Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. How...

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-a...