Arrhythmias

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures.

Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level ...

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis.

Horizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of t...

Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convolutional networks in previous studies, and to impr...

Efficacy and Safety of Appropriate Shocks and Antitachycardia Pacing in Transvenous and Subcutaneous Implantable Defibrillators: Analysis of All Appropriate Therapy in the PRAETORIAN Trial.

BACKGROUND: The PRAETORIAN trial (A Prospective, Randomized Comparison of Subcutaneous and Transvenous Implantable Cardioverter Defibrillator Therapy)...

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases...

Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems.

BACKGROUND AND OBJECTIVES: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-...

Robotic, totally endoscopic atrial septal defect repair.

Atrial septal defect accounts for 10-15% of congenital heart disease cases. Small-diameter atrial septal defects diagnosed during infancy or early adu...

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem fo...

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.

Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical me...

Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the auto...

Review of Deep Learning-Based Atrial Fibrillation Detection Studies.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiogr...

Robustness of convolutional neural networks to physiological electrocardiogram noise.

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning method...

Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.

Deep learning (DL) has achieved promising performance in detecting common abnormalities from the 12-lead electrocardiogram (ECG). However, diagnostic ...

A community effort to assess and improve computerized interpretation of 12-lead resting electrocardiogram.

Computerized interpretation of electrocardiogram plays an important role in daily cardiovascular healthcare. However, inaccurate interpretations lead ...

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation.

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical sy...

Effect of Lamotrigine on Ouabain-Induced Arrhythmia in Isolated Atria of Guinea Pigs.

Lamotrigine (LTG) is an antiepileptic drug used in the treatment of seizures, mood disorders, and cognitive problems. The cardiac effects of LTG, suc...

Prediction of In Vivo Laser-Induced Thermal Damage with Hyperspectral Imaging Using Deep Learning.

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches...

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning.

The electrocardiogram (ECG) is one of the most widely used diagnostic instruments in medicine and healthcare. Deep learning methods have shown promise...

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy.

BACKGROUND: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote I...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized b...