Arrhythmias

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.

Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs. In this study, we exp...

Small Steatotic HCC: A Radiological Variant Associated With Improved Outcome After Ablation.

Percutaneous thermal ablation is a validated treatment option for small hepatocellular carcinoma (HCC). Steatotic HCC can be reliably detected by magnetic resonance imaging. To determine the clinical relevance of this radiological variant, we included 235 patients (cirrhosis in 92.3%, classified Child-Pugh A in 97%) from a prospective database on percutaneous thermal ablation for <3 cm HCC. Among ...

Lower, Variable Intrathecal Opioid Doses, and the Incidence of Prolonged Fetal Heart Rate Decelerations After Combined Spinal Epidural Analgesia for Labor: A Quality Improvement Analysis.

BACKGROUND: Combined spinal-epidurals with low-dose intrathecal opioids and local anesthetics are commonly used to initiate labor analgesia due to the...

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices.

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac impla...

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The ob...

Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms.

We present the results from a white-box machine learning approach to detect cardiac arrhythmias using electrocardiographic data. A C5.0 is trained to ...

Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.

BACKGROUND: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal...

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.

INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diver...

Application of a machine learning algorithm for detection of atrial fibrillation in secondary care.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-ri...

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. ...

Noise robust automatic heartbeat classification system using support vector machine and conditional spectral moment.

Heartbeat classification is central to the detection of the arrhythmia. For the effective heartbeat classification, the noise-robust features are very...

Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography.

Rapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; h...

Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

BACKGROUND: An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the...

Deep learning for digitizing highly noisy paper-based ECG records.

Electrocardiography (ECG) is essential in many heart diseases. However, some ECGs are recorded by paper, which can be highly noisy. Digitizing the pap...

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. Ho...

Substrate-Free Multilayer Graphene Electronic Skin for Intelligent Diagnosis.

Current wearable sensors are fabricated with substrates, which limits the comfort, flexibility, stretchability, and induces interface mismatch. In add...

Temporary pacemaker insertion for severe bradycardia following pneumoperitoneum during robot-assisted radical prostatectomy: a case report.

BACKGROUND: Pneumoperitoneum to maintain a constant gas flow to assist various surgeries is known to cause severe bradycardia and has been linked to h...

Machine learning-based QSAR models to predict sodium ion channel (Na 1.5) blockers.

Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive ...

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design.

OBJECTIVE: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previou...

The Clinical Application of the Deep Learning Technique for Predicting Trigger Origins in Patients With Paroxysmal Atrial Fibrillation With Catheter Ablation.

BACKGROUND: Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation ablation. Elimination ...